Risk-Based Mass Loading Limits for Solvents 

in Disposed Wipes and Laundry Sludges 

Managed in Municipal Landfills 
 

Prepared under contract to the U.S. Environmental Protection Agency, 

Office of Resource Conservation And Recovery 

Mail Code 5305P 

1200 Pennsylvania Ave. N.W. 

Washington, D.C. 20460 

inter alia, Contract EP-W-09-004, Work Assignment 0-01 

by: 

RTI International 

P.O. Box 12194 

Research Triangle Park, NC 27709 

October 2, 2009 
 

[This page intentionally left blank] 
 

Table of Contents

RISK-BASED MASS LOADING LIMITS FOR SOLVENTS IN DISPOSED WIPES AND 

LAUNDRY SLUDGES MANAGED IN MUNICIPAL LANDFILLS— EXECUTIVE 

SUMMARY...................................................................................................................... 1

Purpose and Scope of the Risk Analysis....................................................................................................................1

Risk Analysis Methodology.........................................................................................................................................2

Risk-Based Mass Loading Limits...............................................................................................................................7

1.0 INTRODUCTION.................................................................................................. 1

1.1 Background....................................................................................................................................................1

1.2 Purpose...........................................................................................................................................................1

1.3 Methodology Overview.................................................................................................................................2

1.4 Document Organization................................................................................................................................4

2.0 PROBLEM FORMULATION ................................................................................ 1

2.1 Conceptual Model .........................................................................................................................................2

2.2 Analysis Plan..................................................................................................................................................4

3.0 ANALYSIS ........................................................................................................... 1

3.1 Landfill Source Modeling and Characterization........................................................................................2

3.1.1 Conceptual Approach .................................................................................................................................2

3.1.2 Model Inputs.............................................................................................................................................11

3.1.3 Source Model Outputs..............................................................................................................................20

3.2 Air Dispersion Modeling.............................................................................................................................28

3.2.1 Conceptual Model ....................................................................................................................................28

3.2.2 Model Inputs.............................................................................................................................................29

3.2.3 Model Outputs..........................................................................................................................................31

3.3 Groundwater Modeling ..............................................................................................................................31

3.3.1 Conceptual Model ....................................................................................................................................31

3.3.2 Model Inputs.............................................................................................................................................41

3.3.3 Model Outputs..........................................................................................................................................42

3.4 Shower Modeling.........................................................................................................................................43

3.4.1 Conceptual Model ....................................................................................................................................43

3.4.2 Model Inputs.............................................................................................................................................46

3.4.3 Model Outputs..........................................................................................................................................47

3.5 Exposure Modeling .....................................................................................................................................47

3.5.1 Conceptual Model ....................................................................................................................................47

3.5.2 Model Inputs.............................................................................................................................................52

3.5.3 Model Outputs..........................................................................................................................................54
 

3.6 Human Health Effects Modeling................................................................................................................54

3.6.1 Conceptual Model ....................................................................................................................................54

3.6.2 Model Inputs.............................................................................................................................................59

3.6.3 Model Outputs..........................................................................................................................................61

4.0 RISK CHARACTERIZATION............................................................................... 1

4.1 Human Health Risk.......................................................................................................................................1

4.1.1 Ambient Air................................................................................................................................................2

4.1.2 Groundwater...............................................................................................................................................2

4.2 Uncertainty Characterization.....................................................................................................................12

4.2.1 Conceptual Approach ...............................................................................................................................13

4.2.2 Model Uncertainty....................................................................................................................................16

4.2.3 Parameter Uncertainty ..............................................................................................................................23

5.0 LANDFILL MASS LOADING RATES.................................................................. 1

5.1 Summary of Risk Analysis Calculations .....................................................................................................1

Step 1. Run the Source Model Using a Constant Initial Concentration ....................................................................2

Step 2. Normalize the Source Model Results to a Constant Mass Loading Rate......................................................2

Step 3. Calculate Risks and Noncancer HQs Based on the Unitized Mass Loading Rate ........................................3

Step 4. Derive the Scaling Factors Needed to Calculate the Risk-Based Mass Loading Rates................................3

Step 5. Calculate the Risk-Based Mass Loading Rates.............................................................................................3

Step 6. Select the Limiting Pathway for Selected Risk Percentiles ..........................................................................4

Step 7. Check Whether the Mass Loading Limits Exceed Solvent Partitioning Limitations....................................4

5.2 Mass Loading Limits.....................................................................................................................................4

6.0 REFERENCES..................................................................................................... 1
Review Draft Table of Contents 

List of Figures 

2-1 Conceptual model for modeling solvents in wipes..............................................................2-2 

2-2 Modeling scenarios evaluated in developing risk-based mass loading rates......................2-4 

2-3 How the Monte Carlo framework addresses uncertainty....................................................2-7 

2-4 Basic Monte Carlo structure................................................................................................2-8 

2-5 Meteorological stations and region boundaries for the contiguous 48 states....................2-15 

3-1 Model representation of operational landfill waste addition...............................................3-4 

3-2 Coupled in-series reactor model of a column of landfill cells.............................................3-5 

3-3 Measured infiltration (water added) and leachate flow rates over time 

 (from Fungaroli and Steiner, 1979).....................................................................................3-8 

3-4 Relationship between the infiltration rate and the gas generation rate..............................3-19 

3-5 Schematic diagram of groundwater modeling scenario.....................................................3-32 

3-6 Schematic plan view showing procedure for determining the downstream location 

 of the receptor well: (a) well location determined using radial distance, Rrw, and 

 angle off center, 2rw; and (b) well location generated randomly within plume limit........3-35 

4-1 Comparison of different numbers of iterations for 2-ethoxyethanol (least volatile).........4-15 

4-2 Comparison of different numbers of iterations for tetrachloroethylene (most volatile)....4-16 

List of Tables 

ES-1 RB-MLLs Based on an HQ of 1 for 15 Solvents Analyzed Only as Noncarcinogens 

(kg/year)............................................................................................................................ ES-8 

ES-2 RB-MLLs Based on a Cancer Risk Level of 10-4 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year) ....................................... ES-9 

ES-3 RB-MLLs Based on a Cancer Risk Level of 10-5 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year)........................................ES-9

ES-4 RB-MLLs Based on a Cancer Risk Level of 10-6 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year)........................................ES-9 

2-1 Exposure Model Outputs by Receptor and Exposure Route.............................................2-10 

2-2 Outputs of the Risk Model by Receptor and Pathway......................................................2-11 

2-3 Hydrogeologic Environments in EPACMTP....................................................................2-14 

3-1 Characteristics of EPACMTP Soils..................................................................................3-14 

3-2 Comparison of Constructed Data Set with Other Sources................................................3-17 

3-3 Relationship Between the Infiltration Rate and the Gas Generation Rate........................3-19 

3-4 Process Summary Results for All Solvents......................................................................3-21 

3-5 Percent Contributions of Primary Loss Mechanisms in the Landfill...............................3-23 

3-6 Percent Mass Loss from the Landfill to the Atmosphere and Groundwater....................3-25 

3-7 Percent Mass Loss from the Landfill Due to No-Cover Diffusion and Liner Leakage...3-26 

3-8 Groundwater Pathway DAFEff

10 ......................................................................................3-39 

3-9 Groundwater Pathway DAFEff

50.......................................................................................3-40 

3-10 Receptors by Exposure Pathway .....................................................................................3-48 

3-11 Human Exposure Factor Input Parameters and Data Sources..........................................3-52 

3-12 Chronic Health Benchmarks Used in the Analysis..........................................................3-62 

4-1 Summary of 50th and 90th Percentile Solvent Human Risk Results at the Unitized 

Mass Loading Rate (1 kg/year): Adult Ambient Air Inhalation Pathway.........................4-3 

Do not cite or quote. viii 
Review Draft Table of Contents 

4-2 Summary of 50th and 90th Percentile Solvent Human Risk Results at the Unitized 

Mass Loading Rate (1 kg/year): Child Ambient Air Inhalation Pathway.........................4-4 

4-3 Summary of 50th Percentile Solvent Wipes Human Risk Results at the Unitized 

 Mass Loading Rate (1 kg/year): Adult Groundwater Exposures.......................................4-5 

4-4 Summary of 90th Percentile Solvent Human Risk Results at the Unitized Mass 

Loading Rate (1 kg/year): Adult Groundwater Exposures................................................4-6 

4-5 Summary of 50th Percentile Solvent Human Risk Results at the Unitized Mass 

Loading Rate (1 kg/year): Child Groundwater Exposures.................................................4-7 

4-6 Summary of 90th Percentile Solvent Human Risk Results at the Unitized Mass 

Loading Rate (1 kg/year): Child Groundwater Exposures.................................................4-8 

4-7 Travel Time Associated with the Peak Exposure Concentration at the Receptor Well....4-10 

4-8 Minimum Mass Loading Rates for Air Pathway Adjusted to Include Leachate Mass 

 (Composite Liner Scenario)..............................................................................................4-19 

5-1 RB-MLLs for Noncarcinogens at the 90th Percentile........................................................5-5 

5-2 RB-MLLs for Noncarcinogens at the 50th Percentile........................................................5-6 

5-3 RB-MLLs for Carcinogens at the 90th Percentile..............................................................5-7 

5-4 RB-MLLs for Carcinogens at the 50th Percentile..............................................................5-8 

Do not cite or quote. ix 
Review Draft List of Acronyms 

List of Acronyms 

3MRA Multimedia, Multipathway, Multireceptor Risk Assessment 

ADD average daily dose 

API American Petroleum Institute 

ATSDR Agency for Toxic Substances and Disease Registry 

CalEPA California Environmental Protection Agency 

CASRN Chemical Abstract Service Registry Number 

CCL clay composite lined 

CDF cumulative distribution function 

CI confidence interval 

CSF cancer slope factor 

CV coefficient of variation 

DAD dermal absorbed dose 

DAF dilution and attenuation factor 

EFH Exposure Factors Handbook 

EMLR estimated mass loading rate 

EMRAD Economics, Methods, and Risk Analysis Division (U.S. EPA) 

EPA Environmental Protection Agency 

EPACMTP Composite Model for Leachate Migration with Transformation Products 

GCL geosynthetic clay lined 

GM geomembrane 

GSCM General Soil Column Model 

HEAST Health Effects Assessment Summary Tables 

HELP Hydrologic Evaluation of Landfill Performance 

HGDB Hydrogeologic Database 

HHW household hazardous waste 

HQ hazard quotient 

HSDB Hazardous Substances Data Bank 

IRIS Integrated Risk Information System 

ISCST3 Industrial Source Complex – Short Term, Version 3 

IWAIR Industrial Waste Air 

LADD lifetime average daily dose 

LandGEM Landfill Gas Emissions Model 

LDS leak detection system 

LFCR landfill coupled reactor 

LMOP Landfill Methane Outreach Program 

MLR mass loading rate 

MOCLA Model for Organic Compounds in Landfills 

MRL minimal risk level 

MSWLF municipal solid waste landfill 

NCEA National Center for Environmental Assessment 

NOAEL no observed adverse effects level 

OMB Office of Management and Budget 

OSWER Office of Solid Waste and Emergency Response 

PPRTV Provisional Peer Reviewed Toxicity Value 

Do not cite or quote. x 
Review Draft List of Acronyms 

RB-MLL risk-based mass loading limit 

RCRA Resource Conservation and Recovery Act 

REL reference exposure level 

RfC reference concentration 

RfD reference dose 

SAB Science Advisory Board (U.S. EPA) 

SCDM Superfund Chemical Data Matrix 

UAC unit air concentration 

URE unit risk estimate 

URF unit risk factor 

VOC volatile organic compound 

WBJ Waste Business Journal 

WMU waste management unit 

Do not cite or quote. xi 
Review Draft Executive Summary 

Risk-Based Mass Loading Limits for 

Solvents in Disposed Wipes and Laundry Sludges Managed in 

Municipal Landfills— 

Executive Summary 

F-Listed Solvents 

1,1,2-Trichloroethane 

1,2-Dichlorobenzene 

2-Ethoxyethanol 

Acetone 

Benzene 

Butanol 

Chlorobenzene 

Cresols 

Cyclohexanone 

Ethyl acetate 

Ethyl benzene 

Isobutanol 

Methanol 

The U.S. Environmental Protection Agency (EPA) Office of Solid Waste and Emergency 

Response (OSWER) is evaluating the management of solvent-contaminated wipes and industrial 

laundry sludge in “nonhazardous waste landfills.” For this analysis, the term “nonhazardous 

waste landfills” refers specifically to municipal solid waste landfills (MSWLFs), which were 

identified as the disposal destination for the solvent-contaminated wipes and laundry sludges. 

The waste from the use of wipes at facilities across the United States is 

managed according to existing state and federal regulations. Many states 

currently exclude laundered wipes from the definition of solid and/or 

hazardous waste, while disposed wipes are regulated as hazardous waste. 

To support a proposed change in the regulatory status of disposed 

solvent-contaminated wipes and laundry sludges, EPA developed a risk 

analysis to characterize the potential risks associated with the disposal of 

wipes and laundry sludge in nonhazardous waste landfills. The analysis 

estimated the amounts of solvents that, if contained on disposed wipes or 

in laundry sludge, would not exceed the evaluated risk criteria if these 

materials were disposed of in nonhazardous waste landfills. 

Methyl ethyl ketone 

Methyl isobutyl ketone 

Methylene chloride 

Tetrachloroethylene 

Toluene 

Trichloroethylene 

Xylene 

This report describes the results of a probabilistic risk analysis of 

solvent-contaminated wipes that was used to produce a distribution of 

risk and hazard estimates with which to calculate a protective mass 

loading rate for each individual solvent. A probabilistic approach is 

particularly appropriate for the development of risk-based mass loading 

rates because solvent-contaminated wipes and laundry sludge could be 

disposed of in landfills of varying geometry and located in many 

different parts of the country. The probabilistic approach, as implemented, does not separate 

sources of uncertainty from sources of variability largely because of the limitations in available 

data needed to parameterize the model. Thus, the approach primarily addresses variability in 

waste management practices, environmental settings (e.g., hydrology, meteorology), and 

exposure-related parameters, such as distance from the landfill and inhalation rates. 

Purpose and Scope of the Risk Analysis 

The purpose of this risk analysis is to estimate the amounts of specific F001–F005 listed 

solvents regulated by the Resource Conservation Recovery Act (RCRA) (40 CFR 261.31) that 

can be disposed of in composite lined and unlined MSWLFs without exceeding the evaluated 

risk criteria at the point of exposure. For this analysis, EPA evaluated both 50th and 90th

percentile risk criteria to support the development of risk-based, solvent-specific mass loading 

limits for national application. Specifically, the criteria stipulate that the allowable solvent mass 

from disposed wipes and laundry sludges managed in MSWLFs in the contiguous United States 

must 

Do not cite or quote. ES-1 
Review Draft Executive Summary 

 Ensure that a selected proportion (e.g., 90 percent) of the hypothetical individuals living near 

a landfill will not be exposed to solvent releases at levels of concern. The levels of concern 

depend on the type of effect: 

– For solvents evaluated for carcinogenic effects, the levels of concern are defined as an 

excess lifetime cancer risk to individuals above 1 chance in 10,000 (i.e., 10-4 ) through 1 

chance in 1,000,000 (i.e., 10-6 ) 

– For solvents evaluated for noncancer health effects, the level of concern is defined as a 

ratio of predicted intake levels to safe intake levels—the hazard quotient (HQ)—above 1. 

Note that an HQ less than or equal to 1 would be considered to be below a level of 

concern. 

EPA evaluated a 90th percentile risk criterion for the risk-based mass loading limit to be 

protective of 90 percent of hypothetically exposed individuals across all of the landfill sites in 

the United States (Guidance for Risk Characterization, U.S. Environmental Protection Agency, 

1995; accessible at http://www.epa.gov/spc/pdfs/rcguide.pdfwhich states that “For the Agency's 

purposes, high end risk descriptors are plausible estimates of the individual risk for those persons 

at the upper end of the risk distribution,” or conceptually, individuals with “exposure above 

about the 90th percentile of the population distribution”). As recommended in the Guidance, 

EPA also evaluated the 50th percentile results as the central tendency estimate of that risk 

distribution. 

Thus, the “problem statement” for this analysis may be defined as follows: the risk-based 

mass loading limit (i.e., the amount of mass allowed to be managed in any MSWLF) should be 

protective of X percent (i.e., 90) of the hypothetically exposed individuals across all of the 

landfill sites in the United States, with the term “protective” defined in terms of Y excess risk of 

cancer (i.e., < 10-5) and Z hazard (i.e., < 1) for noncancer endpoints. 

To derive the risk-based mass loading limit (RB-MLL) for the allowable amount of 

solvent that can be managed in an unlined or composite-lined landfill in any year, the mass 

loading rate at the 90th percentile of the theoretical distribution of risk was adjusted to the risk 

and/or hazard level specified by the risk criteria. This adjustment or scaling is based on the ratio 

between the theoretical risk/hazard at the 90th percentile and the risk/hazard level specified by 

the risk criteria for cancer and noncancer effects, respectively. Because the modeling framework 

is linear with respect to solvent concentration and mass loading, the theoretical1

 distribution of 

risk is, in effect, a scalable distribution that allows for the estimation of a mass loading limit at 

any percentile of interest with regard to potential risk/hazard. The resulting calculation produces 

a solvent-specific RB-MLL that serves as a critical input for EPA’s decision-making process. 

Risk Analysis Methodology 

Problem Formulation. In the problem formulation phase, we developed a risk analysis 

strategy that reflects the dimensions of the wipes analysis, including spatial and temporal scales 

(e.g., site-based modeling to support a national application); the mechanisms for solvent releases 

(i.e., long-term low level emissions from landfills); the nature of exposures that can be attributed 

to solvent releases (e.g., groundwater ingestion); the types and location of receptors (e.g., 

 

1

 Theoretical in the sense that the modeling results are based on a unitized, not actual, solvent concentration. 

Do not cite or quote. ES-2 
Review Draft Executive Summary 

residents living within 2 km of the edge of the landfill), and the health endpoints of concern (e.g., 

cancer risk). 

The dimensions of the risk analysis—particularly the need to aggregate site-based results for 

national application—suggest that variability in parameter values describing the landfills, 

environmental settings, receptor locations and contact rates may be a significant source of 

uncertainty. To address these sources of variability, we implemented a Monte Carlo approach 

that consisted of 10,000 realizations for each scenario, defined by the 

 Solvent (e.g., acetone) 

 Landfill Type (e.g., lined or unlined) 

 Contact Medium (e.g., groundwater) 

 Exposure Route (e.g., dermal) 

 Receptor (e.g., child). 

For each of the modeling scenarios, we developed a distribution of theoretical risks at a 

unitized mass loading rate of 1 kg solvent in disposed wipes or laundry sludges per year 

managed in any landfill within the study population. Each distribution represents a “scalable” set 

of risk estimates so that we can derive a mass loading limit for any percentile by (1) dividing the 

risk estimate into the risk level established by EPA and (2) multiplying that scaling factor by the 

unitized mass loading rate to derive a risk-based mass loading limit. 

Analysis Phase. In this phase, we implemented the risk analysis strategy by developing a 

modeling approach to estimate the hypothetical risks to adults and children due to (1) the 

inhalation of ambient air and (2) the use of groundwater as a drinking water source and for 

bathing/showering. For ambient air inhalation, the receptor locations were randomly sampled 

from 8 distances from the center of the landfill (from 25 m to 2,000 m) along one of 36 radii at 

10-degree increments. For groundwater exposures, the receptor well locations were located 

within the lateral extent of the groundwater plume with the direction and distance randomly 

sampled from the edge of the municipal landfill up to 1,600 m (in the Analysis Section, see 

pages 3-30 and 3-36 for explanations of how receptor distances were selected for air and 

groundwater modeling, respectively). 

The risk modeling framework consists of a multiple models that are executed in series to 

estimate the distribution of risk. In essence, the modeling process can be summarized as follows: 

1. The landfill source model estimates releases to the air and leachate at a fixed solvent 

concentration (1 kg solvent / m3

 waste) for lined and unlined landfills. For each solvent, the 

landfill simulation produces 10,000 mass loading rates as function of the solvent 

concentration and landfill characteristics. 

2. The source model outputs are normalized to a unitized mass loading rate of 1 kg per year by 

multiplying the result for each realization by a normalization factor. The normalization factor 

is simply the unitized rate (1 kg/yr) divided by the actual mass loading rate for that 

realization. This produces 10,000 source model results at the unitized mass loading rate for 

varying solvent concentrations. 

3. The environmental fate and transport models use the outputs from the landfill source model 

(based on the unitized mass loading rate) to estimate concentrations in groundwater and in 

ambient air for randomly selected receptor locations. 

4. The exposure model uses the estimated concentrations in contact media to estimate the 

solvent levels to which receptors are exposed for (1) ambient air inhalation, and (2) 

Do not cite or quote. ES-3 
Review Draft Executive Summary 

groundwater use as a drinking water source and for bathing/showering. 

5. For each modeling scenario, the risk model estimates the solvent-specific cancer risk and 

noncancer hazard, as appropriate, for children and adults. This produces a theoretical 

distribution of risk at the unitized mass loading rate. 

With the exception of the landfill source model (referred to as the landfill coupled reactor 

model, or LFCR), the models used in this risk analysis have been extensively peer reviewed and 

used to support a number of OSWER risk analysis. For example, the transport of leachate from 

the landfill through the unsaturated and saturated zones was evaluated quantitatively using 

EPA’s Composite Model for Leachate Migration with Transformation Products (EPACMTP) 

(U.S. EPA, 2003a,c,d; 1997b). This model has been reviewed numerous times—most recently by 

EPA’s Science Advisory Board in 2004 (EPA, 2004a)—and has been used to support risk 

assessments at OSWER for over a decade, including the relatively recent listing determination on 

the dyes and pigments wastes which was the subject of internal and external peer review (RTI, 

2003). 

In contrast, the LFCR model was developed specifically for the wipes analysis to 

estimate the solvent-specific air emissions and leaching releases needed by the fate and transport 

models. The LFCR has received several levels of review, including reviews by internal experts 

(i.e., by members of the project team), EPA scientists and engineers, and external peer reviewers 

(as with the rest of the modeling methodology). Based on comments provided by the peer 

reviewers, the LFCR has been modified in several key aspects, including the approach used to 

estimate solvent releases due to diffusion for the unlined landfill scenario. 

We assembled a variety of data sources to build the input data sets required to run the 

modeling framework. When available, we relied on information collected and developed for 

other OSWER risk assessments that (1) have gone through a formal peer review process, (2) 

have been evaluated by EPA’s Science Advisory Board (SAB), or (3) some combination of the 

above. The data development generally followed major Agency guidance documents, including, 

for example, the Risk Assessment Guidance for Superfund Volume 3 Part A: Process for 

Conducting Probabilistic Risk Assessment (RAGS 3A) (U.S. EPA, 2001a) and the Guiding 

Principles for Monte Carlo Analysis (U.S. EPA, 1997g), as well as Probabilistic Techniques in 

Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and 

Inputs (Cullen and Frey, 1999). Sections 3, 4, and 5, supported by the appendices, provide 

significant details regarding the techniques and assumptions that went into the data development. 

The input parameter values and distributions are presented in their entirety in the appendices. It 

is important to note that we made direct use of input data that have previously undergone 

extensive review without necessarily subjecting the data to further revision. For example, the 

data distributions for exposure factors were developed as part of the Multimedia, Multipathway, 

Multireceptor Risk Assessment (3MRA) modeling system, which was extensively reviewed by 

independent experts and EPA’s SAB (e.g., U.S. EPA, 2004a). However, because the 

probabilistic modeling was intended to capture the variability in environmental conditions across 

the contiguous United States, special care had to be taken to ensure that the parameters 

describing the environmental setting were internally consistent. Therefore, this section provides a 

brief overview of how location-based information—specifically related to the hydrogeological 

and meteorological data—was addressed in setting up the input database. To support the LFCR 

model, EPA developed a new database that represents the substantial variability in landfill 

geometry (e.g., surface area), and operating conditions (e.g., operating life). EPA used a variety 

of data sources to characterize landfills from the open scientific literature to expert judgment. 

Do not cite or quote. ES-4 
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All of these databases were processed to generate input data sets that describe each of the 

modeling scenarios run through the Monte Carlo simulations (e.g., solvent, landfill type, contact 

medium, receptor type). Thus, each realization in the theoretical distribution of 10,000 risk 

outputs represents a unique set of input values that were randomly selected with respect to the 

landfill characteristics, location, hydrogeology, receptor location, exposure factors, etc. 

Correlations—particularly correlations among environmental setting characteristics associated 

with the landfill location—were retained as part of the sampling routines. 

Risk Characterization. The risk assessment was conducted to derive a theoretical risk 

distribution (for both cancer and noncancer endpoints) that could be scaled to establish solventspecific RB-MLLs at the risk criteria determined by EPA. The risk criteria, which include the 

selection of a risk percentile from the distribution, reflect EPA’s consideration of the uncertainty 

associated with the risk estimates developed using probabilistic modeling techniques. As 

discussed in Section 4.2, certain assumptions and policy decisions made in response to 

data/model limitations tend to produce mass loading rates that may err on the side of being health 

protective. Risk distributions were generated for individual exposure pathways for children and 

for adults, as well as for combined exposures associated with groundwater usage. The combined 

exposures included drinking water ingestion, inhalation of volatile solvents during showering 

(adult only), and dermal contact during bathing/showering. Risk estimates for the inhalation of 

ambient air were not combined with the groundwater pathway estimates because our analysis of 

the time to peak well concentration showed that these exposures occur at very different time 

scales. Selected percentiles for the theoretical risk distributions for each modeling scenario are 

presented in Appendix A, including both the individual and combined exposure pathways (i.e., 

assuming a unitized mass loading rate of 1 kg per year). The theoretical risk estimates for the 

unitized mass loading rate provided some useful insights into the nature of potential risks that 

could be associated with solvent management in municipal landfills: 

 As expected, the predicted risks for the unlined scenarios were always greater than those for 

the composite-liner scenarios. 

 At the unitized mass loading rate of 1 kg per year, no theoretically predicted risks at the 50th 

or 90th percentiles were above the risk criteria for cancer or noncancer endpoints for either 

the unlined or composite-lined scenarios. 

 For the unlined scenarios, the groundwater pathways were always associated with the highest 

predicted risks at the 90th percentile of the distribution (i.e., the upper end). 

 For the composite-liner scenarios, this trend for highest predicted risks was true for all 

solvents but methylene chloride and methyl ethyl ketone, which showed higher risks for the 

inhalation of ambient air pathway. 

 For solvents evaluated for cancer risks,2 the highest cancer risks were predicted for adult 

receptors; for noncancer-causing solvents, the highest hazard was typically predicted for 

children. 

 The ingestion route of exposure was the most significant route as compared to inhalation or 

dermal contact; however, combined exposures showed the highest risks for roughly one-third 

of the solvents. 

 

2

 These solvents were: benzene (known human carcinogen under U.S. EPA, 1986), methylene chloride (probable 

human carcinogen), tetrachloroethylene (not classified in IRIS), 1,1,2-trichloroethane (possible human carcinogen), 

and trichloroethylene (cancer classification withdrawn and under review). 

Do not cite or quote. ES-5 
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 For the unlined scenarios, the predicted risks at the 50th percentile showed similar trends as 

the predicted risks at the 90th percentile (e.g., groundwater-driven). 

 For the composite-liner scenarios, the highest predicted risks at the 50th percentile were 

based on the inhalation of ambient air pathway for 16 solvents. 

In addition to presenting the theoretical risk distributions, we describe the strengths and 

limitations of the risk analysis methodology, and address several critical comments offered by 

the external peer reviewers.3

 Limitations and uncertainties were primarily addressed through 

qualitative narrative that describes potential bias associated with certain model simplifications, 

data gaps, etc, with respect to the risk estimates (i.e., is risk overestimated?). We developed 

several quantitative analyses to determine the significance of peer-review comments and explore 

the models’ behavior. Ultimately, the risk characterization is intended to provide risk managers 

with information pertinent to the use and interpretation of the theoretical risk distributions. 

Below, we highlight the most important limitations, uncertainties, and analyses considered in the 

risk characterization. 

 Receptor Wells. For each realization, it is implicitly assumed that a potable well is located 

somewhere between the edge of the landfill out to 1,600 m. In addition, the groundwater 

exposure pathways are modeled only for hypothetical individuals that rely on receptor wells 

within the lateral extent of the plume. With respect to the general population, these are 

conservative assumptions that would tend to over-predict the groundwater pathway risk. 

 Landfill Locations. The geographic distribution of landfills was based on a sample of 66 

offsite Industrial D landfill locations, implying that this population is representative of the 

population of MSWLFs in the contiguous United States. The veracity of this assumption is 

unknown. 

 Receptor Locations. The receptor locations differed between the ambient air and 

groundwater exposure pathways. Therefore, we conducted an analysis to determine whether 

limiting the air receptors to the “lateral extent of the air plume” would result in a significant 

change in the risk estimates for ambient air. Based on that analysis, we found that the risk 

estimates would increase by less than a factor of 2 if we modeled only hypothetical receptors 

in the air plume (which would decrease the RB-MLL by the same factor). 

 Monte Carlo Simulation. One peer reviewer suggested that 10,000 iterations may be 

insufficient to characterize the theoretical risk distribution. We performed an analysis of 

convergence at 4,000, 6,000, and 10,000 iterations and demonstrated the stability of the 90th 

percentile in the distribution of 10,000 iterations. 

 Ecological Risk. The potential impacts on aquatic life via the groundwater-to-surface water 

pathway were not modeled. Based on available ecological benchmarks, it appears that 

solvent levels protective of human health will also be protective of aquatic life. 

 Leachate Handling. The LFCR does not simulate a leachate collection system. In response 

to the peer review, we investigated this uncertainty and determined that, because the LFCR is 

mass conserving, the inclusion of leachate collection would not significantly change the risk 

estimates for the inhalation of ambient air pathway. 

 

3

 The entire methodology was externally peer reviewed, and a separate comment-response document has been placed in the 

docket, EPA-HQ_RCRA-2003-0004. 

Do not cite or quote. ES-6 
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 Liner Leakage Rates. The liner leakage rates are based on a relatively small empirical data 

set that represents some subset of liner behavior (Tetra Tech, 2001). Thus, the use of these 

leakage rates is associated with an unquantifiable, and perhaps a significant level of 

uncertainty. 

 Cover/Liner Characteristics. The cover/liner characteristics have a significant influence on 

the calculation of diffusion. The range of geomembrane permeation coefficients is based on 

empirical studies and, although there is no known bias in using the empirical studies, the 

degree to which these data are relevant to the distribution of MSWLFs across the country is 

unknown. 

 Landfill Characteristics. The area, height, and operating life of landfills were derived using 

summary statistics for each parameter from a commercial database (Waste Business Journal) 

obtained by EPA. Because the statistical descriptors were not correlated, randomly selecting 

parameter values as independent variables resulted in unrealistic landfills (e.g., 1 cm depth). 

Therefore, we derived a “representative” set of 10,000 landfills that (1) reflected reasonable 

engineering designs/practices (e.g., height), and (2) were generally consistent with the 

original summary statistics. The degree to which this derived data set represents the actual 

population of MSWLFs is unknown. However, comparisons with other data sources on 

landfill size and operations indicate that this data set is an appropriate representation. 

 Dermal Contact. In response to peer review comments, dermal exposure to solvents during 

bathing/showering was modeled. EPA has identified the skin permeability coefficient 

required to calculate dermal dose as a parameter with a high level of uncertainty. Given the 

fact that drinking water ingestion drives the risk estimates, we do not consider this 

uncertainty to be significant for most solvents. 

 Chemical Properties. Point estimates were used for all chemical properties, including 

biodegradation rates. EPA notes that the paucity of data on biodegradation introduces a high 

level of uncertainty in modeling this process. Because we selected conservative values (i.e., 

lowest biodegradation rate), we believe that the risks are likely to be over-predicted for some 

solvents. 

Health Benchmarks. Point estimates of human health benchmarks were identified in EPAapproved sources. Although these benchmarks are derived to account for differences in intraspecies sensitivity and address the quality and quantity of supporting data, the uncertainty 

associated with their use has not been quantified. However, the primary goal of EPA actions is 

protection of human health; accordingly, as an Agency policy, risk assessment procedures, 

including default options that are used in the absence of scientific data to the contrary, should be 

health protective. Therefore, health benchmarks ensure that risks are not underestimated and 

therefore may lean towards overestimating risks. 

Risk-Based Mass Loading Limits 

The risk-based mass loading rates are calculated based on the 50th and 90th percentiles from 

the theoretical risk distribution. For each modeling scenario (which includes individual exposure 

pathways and combined exposure pathways), the post-processing program 

1. Selects the risk estimates at the 50th and 90th percentiles from the distribution 

2. Calculates the ratio (referred to as the scaling factor) between the risk estimate and the 

appropriate risk or hazard level 

Do not cite or quote. ES-7 
Review Draft Executive Summary 

3. Calculates the risk-based mass loading rate for each scenario by multiplying the unitized rate 

of 1 kg per year by the solvent-specific scaling factor 

4. Checks the risk-based mass loading rate based on the solubility limit (if the solubility limit is 

exceeded, a solubility-based mass loading rate is calculated) 

5. Selects the lowest mass loading rate from among all of the modeling scenarios at the 50th and 

90th percentiles, respectively, and sets this value as the RB-MLL. 

The “scalability” of the unitized mass loading rate to a mass loading rate at a specific 

risk/hazard level is possible because the system is linear with respect to concentration and mass. 

Thus, the solvent-specific RB-MLL that corresponds to the 90th percentile of the theoretical risk 

distribution represents the “probability” (a level of confidence or “certainty”) that a randomly 

selected individual from the population of all individuals living around all landfills will be 

protected for the exposure pathways considered in this risk analysis. In this context, the term 

protective refers specifically to EPA’s risk criteria for cancer risk and noncancer hazard. The 

cancer risk criterion used in this analysis is 10-4 to 10-6,. The lifetime cancer risk range of 10-4 to 

10-6 is well established as a range for risk management purposes (40 CFR 

300.430(e)(2)(i)(A)(2)). 

Table ES-1. RB-MLLs Based on an HQ of 1 for 15 Solvents Analyzed Only as 

Noncarcinogens (kg/year) 

No Liner Composite 

Solvent 50%ilea 90%ilea 50%ilea 90%ilea

1,2-

Dichlorobenzene 

4.2E+04 N 7.1E+02 N 4.0E+06 S 1.4E+06 N 

2-Ethoxyethanol 2.1E+06 N 2.3E+04 N 1.4E+09 S 8.1E+07 N 

Acetone 8.4E+05 N 2.8E+04 N 1.1E+08 S 1.1E+08 S 

Butanol 5.9E+05 N 1.2E+04 N 4.9E+08 S 1.2E+07 N 

Chlorobenzene 3.1E+03 N 5.8E+01 N 1.4E+06 N 2.6E+04 N 

Cresols (total) 9.9E+04 N 1.4E+03 N 7.6E+07 S 1.4E+06 N 

Cyclohexanone 3.7E+06 N 9.9E+04 N 7.5E+06 S 7.5E+06 S 

Ethyl acetate 4.4E+08 S 5.3E+05 N 4.4E+08 S 4.4E+08 S 

Ethyl benzene 1.2E+05 N 2.1E+03 N 8.0E+06 S 2.6E+06 N 

Isobutanol 2.4E+06 N 5.0E+04 N 2.5E+07 S 2.5E+07 S 

Methanol 2.7E+05 N 4.9E+03 N 6.9E+08 S 1.2E+07 N 

Methyl ethyl 

ketone 

8.0E+05 N 1.9E+04 N 3.0E+07 S 3.0E+07 S 

Methyl isobutyl 

ketone 

3.0E+05 N 6.2E+03 N 1.4E+07 S 1.4E+07 S 

Toluene 4.9E+04 N 1.1E+03 N 1.3E+07 S 6.1E+05 N 

Xylene (mixed 

isomers) 

2.3E+04 N 4.8E+02 N 8.5E+06 N 5.4E+05 N 

a N = based on noncancer risk, S = based on solubility limit. 

Do not cite or quote. ES-8 
Review Draft Executive Summary 

Table ES-2. RB-MLLs Based on a Cancer Risk Level of 10-4 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year)

No Liner Composite 

Solvent 50%ilea 90%ilea 50%ilea 90%ilea

1,1,2-Trichloroethane 4.8E+03 N 1.1E+02 C 3.8E+06 C 1.0E+05 N

Benzene 1.0E+03 N 2.2E+01 N 4.9E+05 N 1.4E+04 N

Methylene chloride 3.4E+04 N 8.0E+02 N 6.4E+06 N 9.7E+05 N

Tetrachloroethylene (high)b 9.5E+02 C 1.7E+01 C 1.7E+06 C 8.2E+03 C

Tetrachloroethylene (low)b 1.1E+03 C 2.0E+01 C 1.5E+07 C 9.8E+03 C

Trichloroethylene 1.3E+04 C 2.2E+02 C 5.1E+06 C 1.3E+05 C

a C = based on cancer risk, N = based on noncancer risk. 

b Two estimates are provided for tetrachloroethylene cancer risks to reflect the high 

and low ends of the range of benchmarks. 

Table ES-3. RB-MLLs Based on a Cancer Risk Level of 10-5 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year) 

No Liner Composite 

Solvent 50%ilea 90%ilea 50%ilea 90%ilea

1,1,2-Trichloroethane 5.9E+02 C 1.1E+01 C 3.8E+05 C 1.1E+04 C 

Benzene (high)b 2.9E+02 C 5.0E+00 C 1.4E+05 C 3.6E+03 C 

Benzene (low)b 1.0E+03 N 1.9E+01 C 4.8E+05 C 1.3E+04 C 

Methylene chloride 5.4E+03 C 1.1E+02 C 2.2E+06 C 2.4E+05 C 

Tetrachloroethylene (high)b 9.5E+01 C 1.7E+00 C 1.7E+05 C 8.2E+02 C 

Tetrachloroethylene (low)b 1.1E+02 C 2.0E+00 C 1.5E+06 C 9.8E+02 C 

Trichloroethylene 1.3E+03 C 2.2E+01 C 5.1E+05 C 1.3E+04 C 

a C = based on cancer risk, N = based on noncancer risk. 

b Two estimates are provided for benzene and tetrachloroethylene cancer risks to 

reflect the high and low ends of the range of benchmarks. 

Table ES-4. RB-MLLs Based on a Cancer Risk Level of 10-6 or an HQ of 1 for 5 Solvents 

Analyzed Both as Carcinogens and Noncarcinogens (kg/year) 

No Liner Composite 

Solvent 50%ilea 90%ilea 50%ilea 90%ilea

1,1,2-Trichloroethane 5.9E+01 C 1.1E+00 C 3.8E+04 C 1.1E+03 C 

Benzene (high)b 2.9E+01 C 5.0E-01 C 1.4E+04 C 3.6E+02 C 

Benzene (low)b 1.1E+02 C 1.9E+00 C 4.8E+04 C 1.3E+03 C 

Methylene chloride 5.4E+02 C 1.1E+01 C 2.2E+05 C 2.4E+04 C 

Tetrachloroethylene (high)b 9.5E+00 C 1.7E-01 C 1.7E+04 C 8.2E+01 C 

Tetrachloroethylene (low)b 1.1E+01 C 2.0E-01 C 1.5E+05 C 9.8E+01 C 

Trichloroethylene 1.3E+02 C 2.2E+00 C 5.1E+04 C 1.3E+03 C 

a C = based on cancer risk 

b Two estimates are provided for benzene and tetrachloroethylene cancer risks to 

reflect the high and low ends of the range of benchmarks. 

Do not cite or quote. ES-9 
Review Draft Section 1.0 Introduction 

1.0 Introduction 

1.1 Background 

Approximately two billion wipes are used annually to clean industrial equipment 

contaminated with hazardous solvents. These solvent-contaminated wipes include launderable 

shop towels and rags, paper towels, and disposable wipes 

(collectively called disposed wipes) that are used by various industry 

sectors, such as publishing, printing, and automobile manufacturing 

and maintenance. The waste from the use of wipes at facilities across 

the United States is managed according to existing state and federal 

regulations. Many states currently exclude laundered wipes from the 

definition of solid and/or hazardous waste, while disposed wipes are 

regulated as hazardous waste. 

F001–F005 Listed Solvents 

1,1,2-Trichloroethane 

1,2-Dichlorobenzene 

2-Ethoxyethanol 

Acetone 

Benzene 

Butanol 

Chlorobenzene 

Cresols (total) 

Cyclohexanone 

Ethyl acetate 

Ethyl benzene 

Isobutanol 

Methanol 

Methyl ethyl ketone 

Methyl isobutyl ketone 

Methylene chloride 

Tetrachloroethylene 

To support a proposed change in the regulatory status of 

disposed solvent-contaminated wipes and laundry sludges, EPA 

developed a risk analysis methodology to characterize the potential 

risks associated with the disposal of wipes and laundry sludge in 

nonhazardous waste landfills. The analysis estimated the amounts of 

the F001–F005 listed solvents (regulated by the Resource 

Conservation Recovery Act [RCRA], 40 CFR 261.31) shown in the 

box at right that, if contained on disposed wipes or in laundry sludge, 

would not exceed EPA’s risk criteria if these materials were disposed 

of in nonhazardous waste landfills. This report describes the 

methodology developed to estimate the “allowable” amounts of 

solvents that could be disposed of in nonhazardous waste landfills.4

Toluene 

Trichloroethylene 

Xylene (mixed isomers) 

The risk analysis methodology has undergone an independent scientific peer review by 

experts outside EPA. The peer review focused on technical aspects of the analysis, including the 

construction and implementation of the Monte Carlo analysis, the selection of models to estimate 

the release of solvents from municipal landfills, the subsequent fate and transport of the solvents 

in the environment, and the characterization of risks resulting from potential exposures to human 

receptors. This report includes changes to the methodology that were made in response to those 

comments, and represents a significant effort to improve the overall clarity and transparency of 

the methods, data, and models used to calculate the allowable mass loading rates. A copy of the 

peer reviewers’ comments and the Agency’s responses to these comments can be found in the 

docket (EPA-HQ-RCRA-2003-0004), which is located at www.regulations.gov. 

1.2 Purpose 

This risk analysis was conducted to estimate the amounts of specific F-listed solvents that 

can be disposed of in nonhazardous waste landfills without exceeding EPA’s risk criteria at the 

point of exposure. For this analysis, the term “nonhazardous waste landfills” refers specifically 

to municipal solid waste landfills (MSWLFs), which were identified as the disposal destination 

for the solvent-contaminated wipes and laundry sludge. The amounts are expressed as “mass 

 

4

 In this context, the term “allowable” refers to the maximum amount of solvent that can be landfilled each year and still meet 

EPA’s risk criteria. 

Do not cite or quote. 1-1 
Review Draft Section 1.0 Introduction 

loading rates” (in units of mass added to the landfill per year) and are intended to be protective of 

public health. Based on the results of the risk analysis, mass loading rates were derived for 

modeling scenarios defined in terms of the solvent, landfill type (e.g., lined or unlined), exposure 

pathway (e.g., ambient air inhalation), contact media (e.g., groundwater), and receptor (e.g., 

child or adult). Specifically, the points of comparison used in this risk analysis stipulate that the 

allowable solvent mass from disposed wipes and laundry sludges managed in MSWLFs in the 

contiguous United States must 

 Ensure that 50 or 90 percent of the hypothetical individuals living near a landfill will not 

be exposed to solvent releases at levels of concern. 

The level of concern depends on the type of effect: 

 For carcinogenic (cancer-causing) solvents, the level of concern is defined as an excess 

lifetime cancer risk to individuals of 1 chance in 10,000 to 1 chance in 1,000,000 (i.e., 1 x 

10-4 to 1 x 10-6)

5

 For solvents that cause noncancer health effects, the level of concern is defined as a ratio 

of predicted intake levels to safe intake levels—the hazard quotient (HQ)—above 1. Note 

that an HQ less than or equal to 1 would be considered to be below a level of concern. 

Thus, the “problem statement” for this analysis may be defined as follows: the risk-based 

mass loading limit (i.e., the amount of mass allowed to be managed in any MSWLF) should be 

protective of X percent (i.e., 90) of the hypothetically exposed individuals across all of the 

landfill sites in the United States, with the term “protective” defined in terms of Y excess risk of 

cancer (i.e., 10-5) and Z hazard (i.e., ≤1) for noncancer endpoints. For the ambient air inhalation 

pathway, the exposed individuals are defined as individuals that reside within 2 kilometers (1.2 

miles) of the edge of the landfill; for the groundwater pathway, the exposed individuals are 

assumed to use drinking water wells that are located within 1 mile (~1.6 km) of the edge of the 

landfill, within the lateral extent of the groundwater plume. 

1.3 Methodology Overview 

Mass loading rates were calculated for four exposure pathways identified based on the 

solubility and volatility of the solvents being considered in the wipes analysis: 

 At residential dwellings, inhalation of ambient air containing solvents emitted from the 

landfill 

 Ingestion of solvents that leach from the landfill and migrate through groundwater to 

residential drinking water wells 

 Inhalation of solvents during showering with contaminated groundwater 

 Dermal exposure to solvents during showering or bathing with contaminated groundwater 

(showering for adults and bathing for children). 

Although other exposure pathways are known to exist (e.g., vapor intrusion of volatile 

gases into buildings), we focused the analysis on these exposure pathways to calculate the 

maximum amount of solvent mass that can be landfilled each year and meet the risk criteria. The 

allowable mass loading rates are identified by choosing the lowest numerical value of any of the 

 

5

 The individual excess lifetime cancer risk range considered acceptable by EPA’s Office of Solid Waste and Emergency 

Response (OSWER) is 10-4 to 10-6. In hazardous waste listings, the point of departure for listing a waste is 10-5. 

Do not cite or quote. 1-2 
Review Draft Section 1.0 Introduction 

pathway-specific mass loading rates.6 We believe that this value will be protective for all 

potential exposure pathways associated with management of disposed wipes and laundry sludges 

for human and ecological receptors. 

To address the uncertainty inherent in modeling these exposure scenarios, a Monte Carlo 

modeling framework was implemented to produce a distribution of risk and hazard estimates 

with which to calculate a protective mass loading rate for each individual solvent. A probabilistic 

approach is particularly appropriate for the development of risk-based mass loading rates 

because solvent-contaminated wipes could be disposed of in landfills of varying geometry and 

located in many different parts of the country. The probabilistic approach, as implemented, does 

not separate sources of uncertainty from sources of variability largely because of the limitations 

in available data needed to parameterize the model. Thus, the approach primarily addresses 

variability in waste management practices, environmental settings (e.g., hydrology), and 

exposure-related parameters, such as distance from the landfill and inhalation rates. 

From an overview perspective, the development of allowable mass loading rates can be 

described by the following steps: 

1. Collect data for all model inputs, including the landfill model (e.g., landfill area), air 

dispersion model (e.g., wind speed), groundwater model (e.g., depth to aquifer), exposure 

model (e.g., drinking water ingestion), and constituent-specific properties (e.g., cancer 

benchmarks, solubility constant). 

2. Process the data and, as appropriate, develop data distributions for all parameters that 

were assumed to vary (e.g., landfill geometry) and fixed values for all parameters that 

were not varied (e.g., health benchmarks). 

3. Run the landfill model at a constant solvent concentration to estimate the release of 

solvents into the air and as leachate. 

4. Normalize the results from the landfill model to a unitized mass loading rate, creating a 

distribution of mass releases based on a rate of 1 kg per year. 

5. Using the normalized concentrations of leachate from the landfill model, run the 

groundwater model to estimate the solvent concentrations at the receptor well (referred to 

as exposure point concentrations). 

6. Using the normalized solvent air emissions from the landfill model, run the air dispersion 

model to estimate the ambient air concentrations of solvents at selected distances 

(referred to as exposure point concentrations). 

7. Using the exposure point concentrations, run the exposure models to estimate the 

exposure distributions for each solvent, including exposure to vapor-phase solvents 

during showering and dermal exposure to solvents during bathing. 

8. Using the estimated exposures, run the risk model to generate a distribution of cancer risk 

and noncancer hazard for all scenarios included in the analysis. 

9. Sort the cancer/noncancer results to identify percentiles of interest from the distribution 

(e.g., 50th and 90th percentiles). 

 

6

 Note that for carcinogens, the groundwater pathways are added together during the risk estimation. Therefore, the mass loading

rate for combined exposures to carcinogens would be lower than the pathway-specific mass loading rates for the individual 

groundwater pathways. 

Do not cite or quote. 1-3 
Review Draft Section 1.0 Introduction 

10. Calculate solvent-specific scaling factors for the percentiles of interest based on the ratio 

of the risk level specified by the risk criteria to the predicted risk. 

11. For each solvent, multiply the unitized mass loading rate by the scaling factor for the 

selected percentiles of risk to derive the Risk-Based Mass Loading Limit (RB-MLL), the 

highest allowable solvent mass associated with disposed wipes and laundry sludges that 

can be managed in any given landfill and meet EPA’s risk criteria. 

1.4 Document Organization 

The remainder of this background document is organized into the following sections: 

 Section 2.0, Problem Formulation, presents the conceptual approach for this analysis, 

including a description of the modeling scenarios, the Monte Carlo framework, and the 

models used to estimate allowable mass loading rates. 

 Section 3.0, Analysis, describes the development of the new landfill source model, the 

fate and transport modeling for air and groundwater pathways, the exposure model, and 

the risk calculations. 

 Section 4.0, Risk Characterization, presents the key results from the Monte Carlo 

simulation and discusses the key uncertainties and limitations of the risk analysis 

methodology. 

 Section 5.0, Mass Loading Rates, describes how the results from the risk analysis were 

used to calculate the mass loading rates, and how the allowable mass loading rates were 

identified. 

The appendices provide supplemental technical information and supporting data: 

 Appendix A – Estimated Risks 

 Appendix B – Chemical-Specific Parameters for Landfill Source and Shower Models 

 Appendix C – Landfill Source Model Mathematical Formulation 

 Appendix D – Landfill Source Model Testing 

 Appendix E – Source Modeling Parameters 

 Appendix F – Groundwater Modeling Parameters 

 Appendix G – Unitized Air Concentrations 

 Appendix H – Exposure and Risk Equations 

 Appendix I – Human Exposure Factors. 

Do not cite or quote. 1-4 
Review Draft Section 2.0 Problem Formulation 

2.0 Problem Formulation 

The risk analysis conducted for disposed solvent-containing wipes and laundry sludges 

was designed to support the determination for appropriately managing solvent-contaminated 

wipes and laundry sludges in municipal solid waste landfills. The analysis estimated the amounts 

of spent solvents in disposed wipes and laundry sludges that could be disposed in municipal 

nonhazardous waste landfills without exceeding the risk criteria adopted for this analysis (a 10-4 

to 10-6 individual excess lifetime risk for cancer effects or a hazard quotient [HQ] of one for 

noncancer effects at the 50th and 90th percentile of the probability distribution of risk or hazard). 

EPA defines the 90th percentile risk comparison for the risk-based mass loading limit to be 

protective of 90 percent of hypothetically exposed individuals across all of the landfill sites in 

the United States (Guidance for Risk Characterization, U.S. Environmental Protection Agency, 

1995; accessible at http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf, which states that “For the 

Agency's purposes, high end risk descriptors are plausible estimates of the individual risk for 

those persons at the upper end of the risk distribution,” or conceptually, individuals with 

“exposure above about the 90th percentile of the population distribution”). We also provide 50th 

percentile results as the central tendency estimate of that risk distribution. 

Specifically, we generated a theoretical7 distribution of risk for the pathways most likely 

to lead to the highest exposure based on an initial solvent concentration of 1 kg solvent/m3

 waste. 

The risk-based mass loading rates were calculated at selected risk percentiles (50th and 90th) for 

each solvent and modeling scenario defined in terms of the liner type, exposure route, receptor, 

etc. For each solvent and liner type, the 50th and 90th percentile mass loading rates were 

compared across all of the modeling scenarios, and the lowest value identified. This risk-based 

mass loading rate (in kg solvent/year) is the maximum solvent mass that can be added to a 

landfill in any given year and be considered protective of individuals living near landfills used 

for wipes disposal for the evaluated exposure pathways. 

Defining the objective of the risk analysis as supporting the determination of allowable 

mass loading rates has significant implications with respect to how the risk analysis was 

constructed. For example, we did not model the incidental ingestion of contaminated water for 

recreational swimmers, although it is possible that a groundwater-to-surface-water breakthrough 

could result in a non-zero exposure in certain communities in which municipal landfills are 

located. This scenario was excluded from the risk analysis largely because there are several 

recent landfill scenario risk analyses indicating that this pathway is not a significant contributor 

to the overall risks, and therefore, including this pathway would not change the risk-based mass 

loading rate (e.g., see the draft risk analysis for Coal Combustion Waste [RTI, 2007] and the risk 

analysis for the Dye and Pigment Waste Listing [U.S. EPA, 2003b]). During the problem 

formulation stage, EPA determined that only the most direct exposure pathways should be 

evaluated in developing the risk-based mass loading rates. Similarly, given the resource and time 

constraints, the risk analysis excluded potential impacts on aquatic life due to the groundwaterto-surface-water pathway because (1) this would have required site-specific information on 

nearby water bodies to determine whether or not there were, in fact, valued aquatic attributes that 

should be protected, and (2) based on the physical and chemical properties of the solvents 

 

7

 Theoretical in the sense that the results were based on an assumed concentration rather than on solvent concentrations from 

analytical sampling. 

Do not cite or quote. 2-1
Review Draft Section 2.0 Problem Formulation 

evaluated, we expect that natural dilution and attenuation would reduce the solvent 

concentrations below levels of ecological concern in receiving water bodies.8

Given the objective of the wipes’ risk analysis, the purpose of this problem formulation is 

to provide an understanding of the methodology developed to derive the risk scaling factors and 

explain how these factors were used to calculate the risk-based mass loading rates. The details of 

each of the models used in the risk analysis—as well as the data developed to support those 

models—are presented in Section 3 and the appendices. In this section, we present the two 

critical outcomes of the problem formulation: 

 The conceptual model illustrates the relationships among the landfill source, 

environmental compartments, and human receptors, and describes the modeling scenarios 

that were evaluated in this risk analysis (Section 2.1). 

 The analysis plan presents the overall modeling approach developed for this risk analysis 

and summarizes how the model was implemented; that is, it describes the modeling 

framework and the flow of information through models that simulate the release, 

transport, exposure, risk, and allowable mass loading rates (Section 2.2). 

2.1 Conceptual Model 

The conceptual model depicted in Figure 2-1 is a simplified representation of how 

solvents in disposed wipes and laundry sludge move in the environment, from their release from 

municipal solid waste landfills (MSWLFs), transport through various environmental 

compartments, and contact with environmental media (e.g., groundwater) by human receptors. 

After release from an MSWLF, solvents can be dispersed in ambient air or migrate from the 

leachate into the underlying aquifer. Thus, residents living near an MSWLF could be exposed 

through the inhalation of contaminated air, ingestion of contaminated groundwater as drinking 

water, inhalation of volatile solvents or dermal contact during showering and bathing with 

contaminated groundwater. 

Figure 2-1. Conceptual model for modeling solvents in wipes. 

 

8 Section 4, Risk Characterization, explores these assumptions and addresses several comments provided by peer reviewers.

Do not cite or quote. 2-2
Review Draft Section 2.0 Problem Formulation 

In developing the conceptual model, we considered the physical and chemical properties 

of the solvent constituents of concern, as well as the operating practices of MSWLFs that are 

relevant to environmental releases of organic solvents. As shown in Appendix B, the 20 solvents 

evaluated include a wide range of volatile and semi-volatile organic chemicals, most of which 

have relatively short environmental half-lives (as compared to persistent organic chemicals). As 

with most organic solvents, diffusion and advection into the air are the primary release 

mechanisms into the environment and are the main reason many of these solvents can be used 

only with adequate ventilation. Moreover, a number of these solvents (e.g., benzene, toluene, and 

xylene) have been identified as groundwater contaminants at Superfund and Resource 

Conservation and Recovery Act (RCRA) Corrective Action sites. Consequently, the leaching-togroundwater pathway was also considered a primary release mechanism. The generally low 

values for partition coefficients for these solvents strongly suggests that indirect exposure 

pathways (e.g., plant uptake of vapors and subsequent ingestion of contaminated produce) will 

either be incomplete or contribute negligibly to total exposure. The conclusion that volatile and 

semi-volatile organic solvents are insignificant contributors to risk via indirect exposure 

pathways (for a landfill source) is consistent with other risk analyses of landfill waste 

management scenarios (see for example, the Paints listing risk analysis [U.S. EPA, 2001b]). 

Thus, direct exposure pathways to air and groundwater were the primary focus of the risk 

modeling for wipes and laundry sludge. 

The conceptual model is a particularly important part of the problem formulation because 

it serves as the basis for a number of decisions critical to the design of a risk analysis. For 

example, the release mechanisms from the landfill are dominated by vapor diffusion, advection, 

and leaching. Because no landfill model was identified that simulated these processes 

simultaneously (at a scale and resolution appropriate for a national rulemaking), we developed a 

landfill model specifically for this risk analysis. 

In addition to informing the selection (or development) of appropriate models, the 

conceptual model is also used in identifying the specific scenarios for which the models will be 

run. These modeling scenarios must reflect the relationships shown in Figure 2-1 and be relevant 

to the risk management goals—in this case, the development of risk-based mass loading rates 

that are protective of public health. Thus, we defined the modeling scenarios in terms of 

 Characteristics of the waste (i.e., solvents managed in the MSWLFs) 

 Characteristics of the landfill (i.e., liner type) 

 Contact media (i.e., environmental medium to which humans could be exposed, such as 

air or groundwater) 

 Exposure routes (i.e., how the solvent can be taken up by receptors, such as inhalation or 

ingestion) 

 Receptors of concern (i.e., types of human receptors that can be exposed, such as adults 

or children). 

Whereas the conceptual model shows the processes and environmental compartments that 

are in the modeling domain, the modeling scenario defines the attributes of a specific simulation 

that are relevant to the decision-making process. For example, the results of a simulation for 

acetone released from an unlined landfill followed by the ingestion of contaminated groundwater

by children represent a specific modeling scenario. Figure 2-2 shows attributes used to define 

modeling scenarios. For this risk analysis, we defined about 400 modeling scenarios that were 

Do not cite or quote. 2-3
Review Draft Section 2.0 Problem Formulation 

considered in calculating allowable mass loading rates based on potential health risks. Not all 

possible combinations of the attributes shown in Figure 2-2 were run; for example, solvents 

lacking an inhalation benchmark were not run for inhalation pathways; showering exposures 

were not considered for children; and only the inhalation exposure route was modeled when the 

contact medium was air or shower vapor (and similarly, only the ingestion and dermal exposure 

routes were modeled for groundwater). 

Figure 2-2. Modeling scenarios evaluated in developing risk-based mass loading rates. 

2.2 Analysis Plan 

The analysis plan is organized into four components. Section 2.2.1 presents a short 

discussion of the risk analysis strategy as it pertains to the dimensions of the modeling 

framework, including the spatial and temporal scales, the receptors of interest, and health 

endpoints. Section 2.2.2 describes the probabilistic modeling framework developed for this risk 

analysis, touching on both the theory and mechanics of the analysis. Section 2.2.3 summarizes 

how the model was implemented, briefly describing each of the major modeling components 

(e.g., landfill model, exposure model). Section 2.2.4 provides an overview of how data were 

developed to support the risk analysis modeling. 

2.2.1 Risk Analysis Strategy 

As discussed in Section 1, the risk analysis strategy supports a national rulemaking for 

determining the appropriate management practice for solvent-contaminated wipes and laundry 

sludge in MSWLFs. Consequently, it is important to recognize that the strategy relies on 

representing the most significant potential risks due to long-term, low-level solvent releases from 

MSWLFs; this will ensure that EPA can place a high level of confidence on the protectiveness of 

the allowable mass loading rates. Implicit in this strategy are three key assumptions: (1) shortterm inhalation exposures during unloading and spreading of waste at the landfill will be subject 

to occupational regulations and will therefore be protective for residents living offsite, (2) it will 

take tens or even hundreds of years for a solvent plume to travel from underneath the landfill to a 

nearby drinking water well, and (3) EPA’s human health benchmarks for chronic exposures are 

appropriate effects measures given our modeling scenarios. With these assumptions in mind, we 

can describe the dimensions of the modeling framework as follows: 

Do not cite or quote. 2-4
Review Draft Section 2.0 Problem Formulation 

 Spatial scale. A representative analysis can be conducted using a model with a very low 

level of spatial resolution (e.g., nationally averaged data inputs) or with a more refined 

model at a more precise spatial scale.9 For this risk analysis, we chose to use models at a 

more refined scale (i.e., site-based) and aggregate the results for hypothetical sites across 

the country into a national profile. The spatial boundaries for which exposures were 

assessed were 1.6 km (1 mile) from the edge of the landfill for groundwater wells and 2 

km for ambient air. 

 Temporal scale. Because we were concerned with long-term releases and resulting 

chronic exposures to solvents, the temporal scale for the modeling framework was 

aggregated to provide annual average concentrations in air and groundwater to which 

receptors may be exposed. The landfill model developed for this analysis is capable of 

producing flux estimates at much shorter time steps; however, the temporal scale for the 

overall model chain (e.g., source model to groundwater model to exposure model) is 

based on annual average concentrations and flux rates.

 Exposures. Exposures to ambient air occur in a very different timeframe than exposures 

to groundwater. Therefore, we did not aggregate groundwater and ambient air exposures 

for any constituent of concern. However, for carcinogens, exposures to groundwater via 

drinking water ingestion were added to the inhalation exposure and dermal exposures 

during showering, as appropriate and consistent with EPA guidance. For noncarcinogens, 

exposures to groundwater via these routes were also combined for solvents with 

inhalation and ingestion health endpoints indicating that the target organ and effect are 

independent of the exposure route. 

 Receptors. Residential exposures for adults and children were considered. Although this 

is a site-based analysis, the risk estimates are individual risks, not population-based risks. 

That is, the distribution of risks represents risk estimates for hypothetical individuals for 

specific modeling scenarios rather than the risks to a population living near a landfill that 

manages solvent-containing wipes and laundry sludges. For the groundwater pathways, 

the receptor wells were randomly located downgradient within the lateral extent of the 

plume up to a distance of 1 mile from the edge of the landfill. (See Section 3, page 3-36, 

for further details on groundwater receptor locations.) For the air pathways, the receptors 

were randomly placed along one of 36 radii (at 10-degree increments) and located up to 2 

km (~1.2 miles) from the edge of the landfill. (See Section 3, page 3-30, for further 

details on air receptor locations.) 

 Health endpoints. For chronic exposures to solvents released from MSWLFs, we 

evaluated two types of health endpoints for each constituent of concern: cancer effects 

and noncancer effects. As with exposure, cancer risks were aggregated for the 

groundwater pathway for each solvent. Cancer risks were not summed across all solvents 

that are carcinogens because the intent of this risk analysis was to support solventspecific mass loading rates. Noncancer hazard was aggregated only for those solvents 

with health endpoints relevant to the same target organ and effect: benzene, cresols, and 

methyl ethyl ketone. 

 

9 There are numerous ways to conduct a representative assessment, which span the continuum of very course to very precise 

models. However, we have chosen to use two very basic approaches to illustrate spatial scale for the wipes risk analysis. 

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Review Draft Section 2.0 Problem Formulation 

2.2.2 Risk Modeling Approach 

To address the substantial variability across landfills and locations for a national analysis, 

we adopted a probabilistic modeling approach commonly referred to as a Monte Carlo approach. 

In essence, a Monte Carlo simulation can be performed when probability distributions can be 

developed for some or all of the input parameters. The probability distributions for each 

parameter are sampled to develop a data set of thousands of individual model runs referred to as 

realizations. A Monte Carlo approach is particularly appropriate for a risk analysis of landfills 

with variable characteristics that are located across a wide range of environmental conditions. 

The Monte Carlo simulation for this analysis captures much of the variability in parameters that 

describe the landfill (e.g., operating practices), environmental setting (e.g., meteorology, 

hydrogeology), and human exposure factors (e.g., receptor distance, drinking water ingestion 

rates). This approach also propagates uncertainty associated with parameter values. The 

uncertainty is addressed along with variability but is not described separately in this analysis. 

The Monte Carlo simulation produces a distribution of exposure and risk which, for this 

risk analysis, was based on an assigned initial concentration for each solvent of 1 kg solvent/m3

waste. From this distribution, we can select any percentile from the distribution for the purposes 

of deriving a risk-based mass loading rate (explained in detail in Section 5). The distribution 

represents the variability in risk associated with location, landfill characteristics, and other inputs 

such as drinking water ingestion rates. 

As implemented, the Monte Carlo simulation does not distinguish between uncertainty 

and variability, and because the output distribution primarily represents the variability in the 

input parameters, the Monte Carlo approach addresses what is generally referred to as Type A 

uncertainty (the uncertainty associated with variability) (Hoffman and Hammonds, 1994). In 

essence, distributions were selected to represent variability (e.g., exposure factors) and in some 

cases, to also represent the uncertainty in the true parameter value (e.g., depth to groundwater 

table). The uncertainty around the true mean (or other percentile risk) and variance of the risk 

distribution are not addressed in the sense that the statistical descriptors for each input parameter 

remain constant throughout the simulation, and the model output is a single distribution of risk 

for each solvent. Type B uncertainty—the uncertainty associated with a lack of knowledge—was 

not addressed separately from variability. Type B uncertainty is distinguished from Type A 

uncertainty by conducting the Monte Carlo simulation in two dimensions; this makes it possible 

to estimate a confidence interval around the probability density function (PDF) and therefore 

allows the uncertainty in the overall result to be quantified. 

For national analyses such as this one, it is difficult to separate variability from 

uncertainty (Nauta, 2000), and a two-dimensional probabilistic approach would have been 

impractical in terms of time and resources. For example, the number of simulations that could 

realistically be performed within the modeling chain for landfill-to-groundwater-to-air dispersion 

was prohibitive because of the added time dimension necessary when running the source, fate 

and transport, and exposure models. Although a two-dimensional Monte Carlo framework can 

provide additional insight into uncertainty by separating variability and uncertainty, a onedimensional probabilistic approach was used for the analysis that commingles variability and 

uncertainty into a single dimension (Mokhtari and Frey, 2005). As explained below, the risk 

distribution from the model simulation represents a best estimate of the distribution of risk for a 

unitized mass loading rate and varying solvent concentrations, accounting for multiple sources of 

uncertainty and variability, especially the variability in the input parameter values. 

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Review Draft Section 2.0 Problem Formulation 

Figure 2-3 is a simple depiction of the Monte Carlo framework developed for the risk 

analysis. It shows how the distributions for input parameters were sampled and used to produce 

the PDF and cumulative distribution function (CDF) from which specific percentiles (e.g., 90th 

percentile) can be identified. The parameters A, B, and C each have their own distributions, 

which may represent variability or uncertainty, or both. For each realization of the model, a 

single value was sampled from each input distribution regardless of the type of variation (i.e., 

variability or uncertainty). For each modeling scenario, the simulation produced the PDF of risk 

results, as shown in Figure 2-3 (i.e., the distribution of risks across exposed individuals across all 

sites represented in the analysis). Lastly, the CDF was created and the specific percentiles (e.g., 

90th percentile) were selected and used to derive the risk-based mass loadings. 

Figure 2-3. How the Monte Carlo framework addresses uncertainty. 

For the Monte Carlo simulation, the system was run 10,000 times for each modeling 

scenario, with each realization producing a single result. The site-based approach ensured 

consistency among related parameters for a given realization in the Monte Carlo database, 

avoiding unrealistic parameter combinations. The simulation output for each modeling scenario 

was a distribution of 10,000 values—cancer risk or noncancer hazard—representing the 

distribution of possible outcomes given the underlying variability (and uncertainty) in the data 

used in the analysis. 

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Review Draft Section 2.0 Problem Formulation 

The mechanics of the Monte Carlo modeling framework are depicted in Figure 2-4. The 

figure shows that the landfill source model performed 10,000 iterations for composite-lined 

landfills and 10,000 iterations for unlined landfills, with identical values for all inputs except 

those related to liner type for each realization (i.e., the only difference in the respective data sets 

was the leakage rates). The two sets of 10,000 iterations were then used as input to the air 

dispersion model and the groundwater model, and the simulation was carried through to produce 

a distribution of risk or hazard for each receptor.10

Modeling Scenario

Solvent x Landfill Type x Contact Medium x Exposure Route x Receptor

Drinking 

water

Groundwater

Landfill Source Model

No Liner Composite

Ambient Air

ISCST3 EPACMTP

Solvent

10,000

iterations

Shower/Batha

Ingestion Inhalation

Adult Child

Inhalation

Adult Child

10,000

iterations

Adult

Dermal

Adult Child

a

 Shower for adult, bath for child

Figure 2-4. Basic Monte Carlo structure. 

 

10 In executing the Monte Carlo simulation, results were only passed to the downstream models if a health benchmark was 

available. For example, if a solvent did not have an EPA-approved inhalation health benchmark, the results from the landfill 

source model were not being passed to the air dispersion model. Similarly, the groundwater pathway involving inhalation during 

showering was not run in those cases. 

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Review Draft Section 2.0 Problem Formulation 

2.2.3 Model Implementation 

As suggested in Figure 2-4, the modeling framework developed for this risk analysis 

consists of a series of modules that are executed in a specific order to estimate the distribution of 

risk. The modeling process can be summarized as follows: 

 The landfill source model estimates solvent releases to the air and leachate 

 The environmental fate and transport models estimate concentrations in groundwater and 

ambient air 

 The exposure models estimate the solvent levels to which receptors are exposed 

 The risk model estimates the solvent-specific cancer risk and noncancer hazard, as 

appropriate for the unitized solvent loading rate of 1 kg solvent per year 

 The post-processing program estimates mass loading rates after the modeling simulation 

is complete. 

The major functionality of the models implemented in this risk analysis is described 

briefly below. 

Landfill Source Model. A landfill coupled reactor (LFCR) model was developed for this 

analysis to estimate the solvent-specific air emissions and leachate fluxes needed by the fate and 

transport models. The LFCR model represents the landfill as a series of fully mixed reactors by 

discretizing the landfill into a series of daily cells. The LFCR model describes mass partitioning 

of solvents among the solid, water, and air phases through linear equilibrium relationships 

(Henry’s law and a solid-water distribution coefficient). Thus, the Monte Carlo simulation first 

ran the LFCR model at an initial fixed solvent concentration of 1 kg/m3. After the landfill 

modeling runs were complete, the results for each solvent (i.e., 10,000 iterations for lined and 

10,000 iterations for unlined landfills) were normalized to a constant mass loading rate of 1 kg/yr 

(as mass loading rate is a function of solvent concentration and landfill characteristics). This set 

of results was then used as input for all downstream models, beginning with fate and transport. A 

detailed discussion of the LFCR model is presented in Section 3.1. 

Fate and Transport Models. The Industrial Source Complex – Short Term Version 3 

(ISCST3) air dispersion model was selected to estimate the solvent concentrations in ambient air 

after release from MSWLFs. The unitized air concentrations (UACs) estimated by ISCST3 are 

based on a unit emission rate of 1 μg/s-m2

. The UACs were multiplied by the solvent-specific 

volatile emission rates predicted by the LFCR source model to estimate a time series of annual 

average solvent concentrations in ambient air. These air concentrations were combined into a 

database of annual average concentrations for all modeled locations in the United States, 19 

representative surface areas for MSWLFs, 7 discrete distances from landfill to receptor, and 36 

directions (at 10 degree increments) relative to the landfill. A detailed discussion of the air 

modeling is presented in Section 3.2. 

EPA’s Composite Model for Leachate Migration with Transformation Products 

(EPACMTP) groundwater model (U.S. EPA, 2003a,c,d) was selected to estimate solvent 

concentrations at the receptor well using the leachate estimates produced by the LFCR model. 

EPACMTP simulates steady-state flow in both the unsaturated zone and the saturated zone 

beneath a waste disposal unit—in this case, an MSWLF—to estimate the concentration arriving 

downgradient at the well, which for this analysis was always placed in the contaminant plume. 

Modeling flow as steady state assumes that conditions (e.g., infiltration) are representative of 

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Review Draft Section 2.0 Problem Formulation 

long-term average conditions that do not change over the duration of the simulation. The solvent 

transport, however, is modeled as a time-dependent finite source that takes into account the time 

period over which a landfill operates and releases contaminants, as well as the actual time it may 

take contaminants to reach the receptor well. The output of EPACMTP is a database of annual 

peak and time-averaged solvent concentrations arriving at randomly selected downgradient 

groundwater receptor well locations. A detailed discussion of the groundwater modeling is 

presented in Section 3.3. 

Exposure Model. The exposure model has two primary functions: 

 Calculating the lifetime average daily dose (LADD) and the average daily dose (ADD) 

for the adult and child receptor due to drinking contaminated groundwater from a nearby 

receptor well and the dermal absorbed dose (DAD) due to dermal contact during 

showering (adult) or bathing (child) in contaminated groundwater. The LADD is 

calculated for cancer endpoints and the ADD is calculated for noncancer endpoints; each 

type of dose is a function of several exposure factors, including the drinking water 

consumption rate and exposure duration. The DAD is calculated for both cancer and 

noncancer endpoints and is a function of several exposure factors, including skin surface 

area and event frequency. A detailed discussion of the exposure modeling is presented in 

Section 3.5. 

 Calculating the air concentration to which the adult receptor is exposed due to the 

inhalation of indoor air associated with showering. The shower model is a special 

component of the exposure model developed under previous landfill scenario risk 

analyses (e.g., U.S. EPA, 2003b). The shower model uses the groundwater concentrations 

predicted by EPACMTP to calculate a time-weighted average air concentration for an 

entire day and estimates the annual average air exposure concentration.11 A detailed 

discussion of the shower model is presented in Section 3.4. 

Table 2-1 shows the exposure model outputs passed to the risk model by receptor and 

route of exposure. Note that the solvent concentrations used in estimating risk due to the 

inhalation of ambient air are calculated by ISCST3 and passed directly to the risk model. That is, 

the exposure model is not used in calculating exposure concentrations for the ambient air 

pathway. However, for completeness, the ambient air pathway is shown in the table. 

Table 2-1. Exposure Model Outputs by Receptor and Exposure Route 

Receptor 

Inhalation 

(Ambient Air) 

Ingestion 

(Drinking Water) 

Inhalation 

(Indoor Air—Shower) 

Dermal 

(Shower or batha) 

Adult resident Air concentration 

(from ISCST3) 

ADD (noncancer) 

LADD (cancer) 

Shower air concentration DAD (noncancer) 

DAD (cancer) 

Child resident Air concentration 

(from ISCST3) 

ADD (noncancer) 

LADD (cancer) 

Not evaluated DAD (noncancer) 

DAD (cancer) 

a

 Shower for adult, bath for child.

 

11 We considered only adult receptors in evaluating the shower scenario. As with previous EPA landfill scenario risk 

assessments, we assumed that children would tend to take a higher proportion of baths. Exposure factors for teenagers are similar 

to adults; therefore, including a teenaged cohort would add little value to the risk estimates. 

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Review Draft Section 2.0 Problem Formulation 

For the child receptor, we did not simulate volatilization from the bath and subsequent 

inhalation for two reasons. First, for noncancer endpoints, the hazard is estimated by comparing 

the RfC to the air concentration. Because first principles show that the solvent mass that enters 

the air during showering is much greater than the solvent mass that could enter the air during 

bathing, the use of the RfC will be protective of both adults and children. Second, for cancer 

endpoints, adult exposures have been shown to drive the risks. A review of Equation 3-11 

indicates why that is typically the case: in addition to the solvent concentration in air, the risk 

estimate is based on the ratio between exposure duration and averaging time, which is almost 

always higher for adults than for children. The Risk Assessment Guidance for Superfund Volume 

3 (RAGS) Part E (U.S. EPA, 2004b) states that even though children may be bathing for a longer 

duration, the showering adult remains the most highly exposed receptor. Therefore, the adult 

shower inhalation pathway will provide a sufficiently protective estimate of potential risk/hazard 

from inhalation while bathing for all receptors. 

Risk Model. The outputs from the risk model are shown in Table 2-2; cancer risks are 

the excess risk to an exposed individual (e.g., 1E-05 means an individual’s excess lifetime cancer 

risk of 1 chance in 100,000) and noncancer hazard is the ratio between exposure and a threshold 

that EPA has established that is likely to be without an appreciable risk of adverse effects (this 

ratio is typically referred to as the hazard quotient, or HQ). This module uses human health 

benchmarks developed by EPA for the purposes of assessing chronic risks for noncancer and 

cancer endpoints associated with long-term exposures to chemical constituents. For the 

inhalation of solvents with cancer benchmarks, the cancer risk was estimated by multiplying the 

appropriate benchmark (inhalation Unit Risk Factor [URF]) by the exposure concentration. For 

the inhalation of solvents with noncancer benchmarks, the hazard was estimated simply by 

calculating the ratio between the predicted exposure concentration in air and the health 

benchmark (the Reference Concentration [RfC]). Note that this calculation does not distinguish 

between adult and child receptors. For the ingestion of solvents with cancer benchmarks, the risk 

was estimated by multiplying the LADD by the appropriate benchmark (the Cancer Slope Factor 

[CSF]). For the ingestion of solvents with noncancer benchmarks, the hazard was estimated as 

the ratio of the ADD to the health benchmark (the Reference Dose [RfD]). 

Table 2-2. Outputs of the Risk Model by Receptor and Pathway 

Receptor Ambient Air Pathway Groundwater Pathway 

Adult resident  Cancer Risk 

 Noncancer Hazard 

 Cancer Risk – Ingestion 

 Cancer Risk – Inhalation 

 Cancer Risk – Dermal 

 Combined Cancer Risk – Ingestion + Inhalation + Dermal 

 Noncancer Hazard – Ingestion 

 Noncancer Hazard – Inhalation 

 Noncancer Hazard – Dermal 

 Combined Noncancer Hazard – Ingestion + Inhalation + Dermala

Child resident  Cancer Risk 

 Noncancer Hazard 

 Cancer Risk – Ingestion 

 Cancer Risk – Dermal 

 Combined Cancer Risk – Ingestion + Dermal 

 Noncancer Hazard – Ingestion 

 Noncancer Hazard – Dermal 

 Combined Noncancer Hazard – Ingestion + Dermala

a Combined noncancer hazard was calculated only for solvents with inhalation and ingestion health endpoints indicating 

that the target organ and effect are independent of the exposure route.

Do not cite or quote. 2-11
Review Draft Section 2.0 Problem Formulation 

For the groundwater pathways, we estimated risks for each individual exposure route for 

the adult (ingestion of drinking water, inhalation during showering, dermal contact during 

showering). Because these exposure routes can occur during the same time period for 

carcinogens, we also calculated a combined risk by summing the risks for these three exposure 

routes. For any given solvent, the summation required that (1) an inhalation and ingestion 

benchmark for carcinogenicity were available, and (2) study data did not suggest a portal-ofentry effect. This approach may slightly overestimate combined risk because the model does not 

subtract the solvent mass available for one shower exposure route (e.g., inhalation) from the 

mass available for the other shower exposure route (e.g., dermal contact). For children, we 

estimated the combined cancer risk due to ingestion of drinking water and dermal contact during 

bathing by summing the route-specific risk estimates. 

The noncancer hazards were summed for the groundwater pathways only for solvents 

with inhalation and ingestion health endpoints indicating that the target organ and effect are 

independent of the exposure route. 

A detailed discussion of the risk model is presented in Section 3.6. 

Mass Loading Rate Model. The model developed to calculate risk-based mass loading 

rates is relatively straightforward and relies on the fact that the risk modeling framework 

described above is linear with respect to both solvent concentration and mass loading rate. Given 

the linearity of the system, the simplest and most transparent way to calculate a risk-based mass 

loading rate is to derive a scaling factor. From the hypothetical distributions for cancer risk and 

noncancer hazard, we calculated the ratio of the estimated risk/hazard at specific percentiles to 

the risk/hazard criteria. We then used this ratio as a scaling factor to increase or decrease the 

unitized mass loading rate, resulting in a mass loading limit at that specific percentile of risk, 

referred to as the risk-based mass loading limit (RB-MLL). For example, for each modeling 

scenario, the RB-MLL at the 90th percentile risk is considered protective of 90 percent of the 

hypothetical exposed individuals at the relevant risk criterion. For example, if the 90th percentile 

cancer risk for wipes contaminated with chlorobenzene was 5 per 100,000 (i.e., 5E-05), the 

scaling factor would be 0.2 (1E-05 / 5E-05). Multiplying the unitized mass loading rate of 1 kg 

solvent/year by the scaling factor of 0.2 results in a risk-based mass loading rate of 0.2 kg/year; 

this is the allowable mass loading rate that is presumed to be protective of 90 percent of the 

individuals exposed to chlorobenzene-containing wipes managed in municipal landfills. 

2.2.4 Data Development 

To support the Monte Carlo simulation, we used a variety of data sources. In particular, 

we relied on information collected and developed for risk analyses that (1) have gone through a 

formal peer review process, (2) have been evaluated by EPA’s Science Advisory Board (SAB), 

or (3) some combination of the above. The probabilistic modeling approach and data 

development generally follow major Agency guidance documents, including, for example, the 

Risk Assessment Guidance for Superfund Volume 3 Part A: Process for Conducting Probabilistic 

Risk Assessment (RAGS 3A) (U.S. EPA, 2001a) and the Guiding Principles for Monte Carlo 

Analysis (U.S. EPA, 1997g), as well as Probabilistic Techniques in Exposure Assessment: A 

Handbook for Dealing with Variability and Uncertainty in Models and Inputs (Cullen and Frey, 

1999). Sections 3, 4, and 5, supported by the appendices, provide significant details regarding 

the techniques and assumptions that went into the data development. The input parameter values 

and distributions are presented in their entirety in the appendices. It is important to note that we 

Do not cite or quote. 2-12
Review Draft Section 2.0 Problem Formulation 

made direct use of input data that have previously undergone extensive review without 

necessarily subjecting the data to further revision. For example, the data distributions for 

exposure factors were developed as part of the Multimedia, Multipathway, Multireceptor Risk 

Assessment (3MRA) modeling system, which was extensively reviewed by independent experts 

and EPA’s SAB (e.g., U.S. EPA, 2004a). However, because the probabilistic modeling was 

intended to capture the variability in environmental conditions across the contiguous United 

States, special care had to be taken to ensure that the parameters describing the environmental 

setting were internally consistent. Therefore, this section provides a brief overview of how 

location-based information—specifically related to the hydrogeological and meteorological 

data—was addressed in setting up the input database. 

The development of the input data set to support a location-based Monte Carlo simulation 

began with the selection of MSWLF locations. We used an approach (and data) similar to that 

used in the peer reviewed risk analysis to derive allowable mass loading rates for the dye and 

pigment industry (U.S. EPA, 2003b). Lacking information on the spatial distribution of 

MSWLFs, municipal landfill locations for this risk analysis were based on the locations of 

industrial landfills found in EPA’s Industrial D database (U.S. EPA, 1987). As with the dyes 

analysis, we implicitly assumed that offsite Industrial D landfills provide a reasonable 

representation of the distribution of MSWLFs across the United States. The subset of 66 offsite 

landfills12 was equally weighted and sampled 10,000 times to obtain a sample population of 

locations and correlated parameters (e.g., aquifer type, climate center, soil types, aquifer 

temperature) necessary to run the LFCR, EPACMTP, and ISCST3 models. 

The databases developed to support the EPACMTP groundwater model were used as the 

primary source for these correlated environmental parameters. EPACMTP has been the subject 

of multiple peer reviews and has been through a formal review process with EPA’s SAB. In 

addition, the EPACMTP and supporting databases have been used in at least eight risk analyses 

that have been through a combination of peer and SAB reviews (e.g., the chlorinated aliphatics 

listing; the Surface Impoundment Study). Thus, we used these data with a high level of 

confidence that the methods used to collect and develop this information were consistent with 

Agency guidance and best practices with respect to probabilistic risk analysis. 

Two major groups of input data used by the fate and transport models required careful 

correlation; these are described below. 

Hydrogeologic Environments. The LFCR and EPACMTP models require a number of 

hydrogeologic parameters. Therefore, the input data set was developed using the correlated, 

national aquifer property data from the American Petroleum Institute (API) Hydrogeologic 

Database (HGDB) by associating each of the landfill locations with a hydrogeologic 

environment (e.g., Newell et al., 1990). EPACMTP uses the HGDB data to specify probability 

distributions for each of the following hydrogeologic parameters: 

 Unsaturated zone thickness 

 Aquifer thickness 

 Hydraulic gradient 

 Longitudinal hydraulic conductivity. 

 

12 The 790 sites in the landfill database were segregated into onsite units (724 sites, approximately 92 percent of the sites) and

offsite units (66 sites, approximately 8 percent) using Table E-1 in Appendix E of the Paints listing risk analysis (U.S. EPA, 

2001b). 

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Review Draft Section 2.0 Problem Formulation 

HGDB provides correlated hydrogeologic data for approximately 400 hazardous waste 

sites nationwide, and classifies these sites into the 12 hydrogeologic environments shown in 

Table 2-3. Empirical distributions for hydrogeologic parameters associated with each of these 

environments are provided in Appendix F. 

Table 2-3. Hydrogeologic Environments in EPACMTP 

Code Description 

01 Metamorphic and igneous 

02 Bedded sedimentary rock 

03 Till over sedimentary rock 

04 Sand and gravel 

05 Alluvial basins valleys and fans 

06 River valleys and floodplains with overbank deposits 

07 River valleys and floodplains without overbank deposits 

08 Outwash 

09 Till and till over outwash 

10 Unconsolidated and semi-consolidated shallow aquifers 

11 Coastal beaches 

12 Solution limestone 

Source: Newell et al. (1990) 

Each of the 66 landfill locations included in the risk analysis was assigned to one of the 

12 hydrogeologic environments. Once the assignment was made, we conducted a preprocessing 

run with EPACMTP to construct a set of randomly generated but correlated hydrogeologic 

parameter values for each landfill location. 

Meteorological Data. Running ISCST3 requires surface and upper air meteorological 

input data; we identified EPA’s Industrial Waste Air (IWAIR) model as a ready source of this 

information. As with the other models used in this risk analysis, IWAIR has been subjected to an 

extensive peer review process. To ensure internal consistency in using these data, we matched 

the most representative IWAIR meteorological region with each of the 66 landfill locations. To 

determine which IWAIR meteorological region was most representative, we cross-referenced the 

landfill locations to EPACMTP climate centers. Next, the EPACMTP climate centers were 

cross-referenced to one of 57 IWAIR meteorological regions (U.S. EPA, 2002a) shown in 

Figure 2-5, with a single meteorological station representing each meteorological region. The 

final selection of the meteorological station was based on two important considerations. First, 

overlapping contiguous areas were evaluated to determine whether they were similar enough that 

they could reasonably be represented by one meteorological station. Surface-level 

meteorological data (e.g., wind patterns), physiographic features (e.g., mountains, plains), and 

land cover (e.g., forest) were all considered in this determination. Second, characteristics of the 

respective climate center and the IWAIR meteorological region were considered, including 

industrial activity, population density, location within the area, years of meteorological data 

available, and average wind speed. Additional details on the selection process are included in 

Appendix G. 

Do not cite or quote. 2-14
Review Draft Section 2.0 Problem Formulation 

Figure 2-5. Meteorological stations and region boundaries for the contiguous 48 states.

Do not cite or quote. 2-15
Review Draft Section 3.0 Analysis 

3.0 Analysis 

This analysis estimates risks from solvents disposed of in municipal landfills. Solvents 

can be released from landfills into the surrounding environment through several different 

mechanisms, including volatilization to the atmosphere and leaching from the landfill with 

subsequent migration through the unsaturated zone and into an underlying aquifer. 

For the analysis, we used computer-based models and sets of equations to estimate the 

risk to human health from current disposal practices. These models include 

 Source models that simulate the release of solvent to air or in leachate from landfills 

 Fate and transport models that estimate solvent concentrations in ambient air and 

groundwater 

 Exposure models that estimate doses for humans exposed to solvents in ambient air, 

groundwater, and shower and bathroom air while showering with contaminated 

groundwater 

 Risk models that calculate cancer and noncancer risks to humans. 

This section describes the models and equations used to calculate exposure point 

concentrations and risk. Section 3.1 describes the landfill source model used to predict the 

environmental release of solvents in disposed wipes and laundry sludge from municipal landfills. 

Sections 3.2, 3.3, and 3.4 describe the fate and transport models used to predict solvent 

concentrations in ambient air, groundwater, and shower and bathroom air, respectively. Section 

3.5 describes the human exposure calculations. Section 3.6 describes the health benchmarks used 

to develop human risk estimates and describes how these risks were calculated for human 

receptors. 

Supporting detail can be found in the following appendices: 

 Appendix A, Risk Estimates, presents the solvent-specific risk estimates associated with 

a constant mass loading of 1 kg/yr for a series of percentiles (50th, 75th, 90th, 95th, and 

99th). 

 Appendix B, Chemical-Specific Parameters for Landfill Source and Shower Models, 

provides input parameter values and distributions used for chemical-specific parameters. 

 Appendix C, Landfill Source Model Mathematical Formulation, presents the detailed 

formulation of the LFCR source model, including the underlying equations and the 

numerical solution algorithm. 

 Appendix D, Landfill Source Model Testing, presents testing results for the LFCR 

source model, including model verification and robustness testing. 

 Appendix E, Landfill Source Modeling Parameters, presents the source model input 

parameter values and distributions. 

 Appendix F, Groundwater Modeling Parameters, presents the input values and the 

distributions used in modeling the groundwater pathway using EPACMTP. 

 Appendix G, Unitized Air Concentrations (UACs), describes the linear interpolation 

methodology used in determining the UAC values. 

Do not cite or quote. 3-1 
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 Appendix H, Equations, presents the equations that were used to estimate ambient air 

concentrations, shower air concentrations, exposure (dose), and risk. 

 Appendix I, Human Exposure Factors, documents the human exposure parameters. 

3.1 Landfill Source Modeling and Characterization 

This section describes the methodology and the model (Lowry et al., 2008) used to 

predict the mass loss of solvent from the landfill to air and the unsaturated zone. Section 3.1.1

provides an overview of the conceptual model, including the basic approach and assumptions. 

Section 3.1.2 discusses the input parameters and values used in this risk analysis. Section 3.1.3

discusses the model outputs. Appendices B–E, listed below, provide additional information 

about the LFCR model and its implementation for this risk analysis: 

 Appendix B, Chemical-Specific Parameters for Landfill Source and Shower Models, 

provides input parameter values and distributions used for chemical-specific parameters. 

 Appendix C, Landfill Source Model Mathematical Formulation, presents the detailed 

formulation of the source model, including the underlying equations and the numerical 

solution algorithm. 

 Appendix D, Landfill Source Model Testing, presents testing results for the LFCR 

landfill source model, including model verification and robustness testing. 

 Appendix E, Landfill Source Modeling Parameters, presents the source model input 

parameter values and distributions not presented elsewhere. 

3.1.1 Conceptual Approach 

The landfill source model estimates the mass loss of solvent from the landfill to air and 

the unsaturated zone for subsequent, media-specific transport modeling. The LFCR source model 

was developed for this analysis as an alternative to the General Soil Column Model (GSCM) 

(U.S. EPA, 1999a), the landfill model employed by the 3MRA modeling system. The LFCR 

model addresses two requirements not considered in the GSCM approach: (1) the model is 

capable of simulating the effects of landfill gas on transport, and (2) the model framework 

reflects typical operating practices at municipal landfills. Gas production in landfills associated 

with waste biodegradation can lead to significant volatile mass loss and can be a dominant 

transport mechanism (Kjeldsen and Christensen, 2001); however, GSCM does not include a 

flowing gas phase. There was also concern that the GSCM approach to waste addition (i.e., 

columns of annual waste) did not capture potentially important transport processes (e.g., the 

volatile mass loss when smaller, daily volumes of waste are added to the landfill). 

3.1.1.1 Overview 

This section provides a basic overview of the LFCR model design and capabilities. 

Appendix C provides a more comprehensive, mathematical formulation of the model. The 

LFCR model is based on a fundamental mass balance approach, whereby chemical constituent 

mass is added to the landfill along with the waste and then undergoes mass transport and 

transformation within the landfill. Chemical mass ultimately remains within the landfill, 

degrades, or migrates from the landfill. 

Do not cite or quote. 3-2 
Review Draft Section 3.0 Analysis 

The LFCR model describes landfill waste as a series of fully mixed reactors. This 

approach is an extension of the peer-reviewed Model for Organic Compounds in Landfills 

(MOCLA), which represents a landfill as a single reactor with constant parameters (Kjeldsen and 

Christensen, 2001; Bartelt-Hunt et al., 2006). The LFCR model extends this approach by 

discretizing the landfill into a series of daily cells, each representing the volume of waste added 

during one day.13 Unlike the MOCLA analytical model, LFCR is a numerical model allowing 

time-variable parameters. A separate, fully mixed reactor represents each daily cell. The daily 

cells are added to a landfill in a series of lifts, as shown in Figure 3-1. Each box in the figure 

represents a daily landfill cell, and each lift is shown in a different color. Once an active landfill 

area (a landfill cell) has been covered with a lift, additional lifts are added vertically. A landfill 

may be divided into a user-specified number of equal-sized landfill cells, which are sequentially 

filled with waste. 

During the operating life of a landfill, the LFCR model adds new daily cells to the top of 

the next appropriate column as dictated by the landfill-filling algorithm (Figure 3-1). Applying 

the approach described above generates a series of adjacent landfill columns. The mass loss from 

an entire landfill to the atmosphere and to the groundwater zone is then the cumulative 

contribution from all columns. 

The LFCR model adopts a mathematical description based on fully mixed reactors and 

applies it within a framework consistent with typical landfill operations (daily cells added in lifts 

as depicted in Figure 3-1). Mass transfer occurs vertically between daily cells and the 

atmosphere and underlying groundwater zone. Mass transfer within each column of daily cells is 

described mathematically using a model of coupled, fully mixed reactors in series. Figure 3-2

portrays such a column of daily cells and the associated mass transfer processes. 

The LFCR model describes mass partitioning between the solid, water, and air phases 

through linear equilibrium relationships (Henry’s law and a solid-water distribution coefficient). 

First-order degradation can occur within the solid, water, and air phases as described by 

independent, phase-specific abiotic and biotic rate constants. (Note that only aqueous phase 

degradation was active for the solvents of concern in this analysis.) 

Diffusive transport can occur vertically between daily cells and the atmosphere and the 

underlying unsaturated zone. The LFCR model can describe diffusive transport through the 

following three types of cover, all of which were used for the analysis: 

 A daily cover, which is typically 6 inches of native soil placed on the waste daily 

 An intermediate cover, which is typically 12 inches of native soil placed when a landfill 

cell is completed and before the final cover is installed 

 A final cover, which is placed over landfill cells at a user-specified time interval after the 

landfill cell operating life; the final cover can be either a soil-only layer or a composite 

layer consisting of a geomembrane and soil. 

 

13 Although the model is designed for daily cells, it can accept cell time periods of other than 1 day. For example, to match the 

analytical solutions, most verification simulations (see Appendix D) have a cell period of 30 years, with a single cell 

characterizing the entire landfill.

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Review Draft Section 3.0 Analysis 

a) First daily cell added to the bottom lift. 

Each box represents one daily cell. Lifts are shown in different colors. The figures show waste addition over time 

until the landfill cell is full. The surface of each daily cell and landfill cell is a square. The daily cell depth can vary 

from the length (however, cubic daily cells are shown here). 

b) Daily cells added laterally in rows. 

c) First lift completed. Second lift started. d) Filled to the fourth lift, fourth row, third cell. 

e) Filled to the fifth lift, sixth row, second cell. f) Landfill cell filled through the seventh lift.

Figure 3-1. Model representation of operational landfill waste addition. 

Do not cite or quote. 3-4 
Review Draft Section 3.0 Analysis 

Qw Sλn

JD,n-1

Cn

JQw,n-1 JQa,n-1

∑

n

ai

i=1

Q

Qa1

JDo JQwo

Qw S C λ1 1

Qw Sλ3

JD,n

C3

JQa,n

.

.

. .

.

.

Qw Sλ2

JD1

C2

JQw1 JQa1

JD2 J JQa2 Qw2

Reactors in blue; cover and liner layers in red. Ci=daily cell concentration; 

Sli=degradation sink term; Qai=landfill gas flow rate; Qw= water infiltration rate;

JQa=Flux in flowing air; JD=Diffusion flux (in air and water); JQw=Flux in flowing water

3

ai

i=1

∑Q

2

ai

i=1

∑Q

Figure 3-2. Coupled in-series reactor model of a column of landfill cells. 

The LFCR model also describes diffusive transport with no cover while waste is actively 

being added. The no-cover diffusive sink term relies on the user-specified fraction of a day with 

no cover, the working face angle, the waste sublayer thickness, and an analytical solution to the 

diffusion equation (see Appendix C for details). 

Do not cite or quote. 3-5 
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The following liner configurations are possible in the LFCR model (only the no liner and 

composite liner options were used for this analysis): 

 No liner 

 Soil liner 

 Composite liner consisting of a geomembrane and soil. 

Linear-equilibrium sorption to the geomembrane can be described using a permeation 

coefficient, which captures the effects of sorption to the geomembrane surface and diffusion 

through the geomembrane (Sangham and Rowe, 2001). Linear-equilibrium sorption to the liner 

and cover soils can be described by dividing the diffusion coefficient by a user-calculated 

retardation coefficient. This analysis did not include sorption within the cover/liner soil layers. 

Advective transport occurs in landfill gas generated through biodegradation of landfill 

waste and in water infiltrating through the landfill (leachate). The model represents upward gas 

and downward water advective transport through advective fluxes between reactors and leaving 

the system through the top and bottom reactors of each column. An exponential decay function 

similar to that of the Landfill Gas Emissions Model (LandGEM) predicts the landfill gas 

generation rate (U.S. EPA, 2005a). The model simulates infiltration conditions before and after 

the final cover is applied with user-specified average infiltration rates. In addition, the user 

specifies a liner leakage rate (before and after the final cover) that describes the rate of advective 

flow through the landfill liner (diffusion through the liner is described as a separate process). The 

infiltration and leakage rates may be different, for example, if a leachate collection system is 

present and collects water from the bottom of the landfill after it has flowed through the waste 

and before it leaks through the liner. For cases with no liner (and thus no leachate collection 

system), aqueous advective transport to the groundwater zone is based on the net infiltration rate 

(the liner leakage rate is not used). Mass transfer to the unsaturated zone equals the sum of the 

diffusive and advective transport from the landfill bottom. Mass transfer to the atmosphere 

equals the sum of advective transport in landfill gas, diffusive transport through the top cover, 

and diffusive transport with no cover. 

The source model numerical solution was based on a Runge-Kutte algorithm for solving 

systems of linear differential equations. The solution time step varied as a function of the 

solution (smaller time steps are required to accurately solve scenarios with greater rates of 

change). Post-processing of the source model results generated time series of annual average flux 

rates (g solvent/yr-m2

) from the landfill to air and the unsaturated zone. These time series 

became input for the air and groundwater fate and transport models. The annual mass loss values 

to air and the unsaturated zone from the source model were averaged over the total surface area 

of the landfill, even though the footprint of the landfill increased over time until all landfill cells 

contained waste. 

3.1.1.2 Key Assumptions 

The LFCR model is based on a relatively simple conceptualization of physical/chemical 

processes in a landfill. Although much more complex models of landfills have been developed, 

the LFCR model describes the mechanisms believed to be of primary importance for this 

national-scale risk analysis. The model achieves a balance between model complexity, 

uncertainty, and implementability. A more complex model would require more extensive and 

detailed input parameters, many of which are not available on a national scale. In addition, the 

Do not cite or quote. 3-6 
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variability and uncertainty in landfill conditions both within a single landfill and among different 

landfills makes the use of a more detailed, mechanistic model inappropriate for this analysis. 

Furthermore, greater complexity would likely lead to additional runtime requirements and 

implementability concerns. 

This section lists some of the recognized model assumptions and limitations that were 

accepted in balancing model complexity, uncertainty, and implementability. Note that the LFCR 

model framework could be extended to account for many of these more complex behaviors (e.g., 

additional heterogeneous and time-variable parameters). However, such complexity was not 

justified or supported by data for this analysis. Source model assumptions and limitations 

included the following: 

 The model allows vertical but no lateral transport. Diffusion can occur in both vertical 

directions, while landfill gas migrates upward and water flows downward. Although this 

approach ignores lateral flow within a landfill, the complexities of 3-D flow (e.g., 

heterogeneities, preferential flow paths) would make a multidimensional model too 

complex for this analysis. In addition, there is a clear precedent for assuming 1-D 

transport within landfill transport models and risk analyses (e.g., Jury et al., 1983, 1990). 

 The model assumes long-term average and homogeneous moisture conditions within the 

waste, liner, and cover layers. The model does not provide a dynamic water balance 

model to simulate varying moisture content in response to short-term weather 

fluctuations. Long-term average moisture content values are considered appropriate and 

sufficient to describe behavior at time scales ranging from decades to hundreds of years. 

Furthermore, an accurate water balance model would need to consider the complexities 

of water movement within landfills (e.g., preferential flow, heterogeneity, short-term 

response to rainfall). The complexity and parameterization of such a complex water 

balance model would be very challenging for a national-scale analysis and might make 

the solution intractable. 

 Without a dynamic water balance, infiltration flow rates in the LFCR model are equal to 

leaching flow rates (water in equals water out). There is no change in water storage 

within the landfill, and the model assumes long-term average moisture conditions within 

the waste and within the cover and liner soils. This assumption deviates from observed 

behavior in some landfills, whereby the waste moisture content increases over time after 

application. The maximum leaching rates occur only after the waste has reached the field 

capacity moisture content. Nevertheless, there is experimental evidence that the field 

capacity is exceeded over long time scales, with water infiltration moving readily through 

preferential flow channels and exiting the landfill as leachate at a similar rate as the 

infiltration. For example, the graph in Figure 3-3 from Fungaroli and Steiner (1979) 

shows measured infiltration and leachate in landfill waste over an approximately 4.5 year 

timeframe. As the graph shows, leachate flow is much less than infiltration during 

approximately the first year and a half as the waste saturates to field capacity. After that, 

the leachate flow rate essentially mimics the infiltration rate, suggesting that field 

capacity moisture content in the waste is maintained over a long time scale and that 

infiltration is flowing readily within the preferential flow pathways. The Fungaroli and 

Steiner (1979) results are based on an experiment performed at a scale smaller than 

typical landfills (specifically, an 8-foot deep waste column); therefore, extrapolation of 

these results to full-scale landfills introduces some uncertainty. Additional experimental 

evidence is available from landfill tracer studies, which show early time breakthrough, 

Do not cite or quote. 3-7 
Review Draft Section 3.0 Analysis 

with the maximum concentrations occurring shortly after breakthrough (Kjeldsen and 

Christiansen, 2001). Therefore, water flow in the LFCR model appears to represent longterm behavior observed in landfills reasonably well. However, there likely are some 

landfills where the waste moisture content regularly fluctuates below the field capacity 

over longer timeframes, and a more complex water balance would be necessary to 

represent the flow dynamics more accurately. In those cases, the LFCR approach 

(infiltration equals leachate flow) is a less realistic approximation that may overestimate 

the mass loss in the leachate. Conversely, this approach may overestimate aqueous 

biodegradation losses by over predicting the mass in leachate. On balance, the approach 

is believed to provide a more conservative estimate for cases where the moisture content 

is below field capacity. Given the uncertainty and the significantly greater complexity 

associated with a dynamic water balance model, the more conservative and simple 

approach was selected. 

Figure 3-3. Measured infiltration (water added) and leachate flow rates over time 

(from Fungaroli and Steiner, 1979). 

 The source model uses long-term average infiltration values before and after the final 

cover is applied. The model does not incorporate short-term temporal variations in 

infiltration (e.g., due to rainfall events). Transport processes can be strongly influenced 

by the infiltration rate (most significantly, aqueous advective transport). Thus, the 

transport may be under - or over predicted relative to fluctuations over shorter time 

scales. However, long-term average rates are believed to provide a good approximation 

over the long time scales of interest for this analysis. 

 Flow patterns within landfills can be complex, with channeled flow and low permeability 

zones leading to some waste that essentially never contacts flowing water. The LFCR 

approach assumes equilibrium partitioning between the air, solid, and aqueous phases of 

Do not cite or quote. 3-8 
Review Draft Section 3.0 Analysis 

the waste, thus neglecting any potential kinetic mass transfer limitations associated with 

relatively less permeable waste and channeled flow. This simplification could lead to 

relatively conservative mass loss estimates, whereby mass more readily migrates from the 

landfill waste into the mobile air and aqueous phases. More complex flow behavior was 

considered during LFCR model development; however, the simpler approach was 

selected to satisfy project requirements, including a short runtime for the Monte Carlo 

analysis, the availability of input parameters, and implementability given the available 

schedule and resources. 

 Gas flow occurs vertically in the model with no dependence on the waste permeability. It 

is well known that gas flow can be inhibited where waste has an increased moisture 

content. Heterogeneous moisture conditions can lead to complex air flow dynamics 

within landfills, including lateral components to flow. Given the model objective of 

describing long-term average conditions at a national scale, detailed air flow dynamics in 

the landfill were not considered. 

 The model assumes a single long-term average waste temperature. Although the 

temperature of waste varies over time, no known, relatively simple models are available 

to describe this complex process. The waste temperature is used to adjust diffusion 

coefficients and Henry’s law constants for temperature effects. If the waste temperature is 

over predicted, diffusive and vapor advective losses would be over predicted. In general, 

landfill waste temperatures should decrease over very long time scales as biodegradation 

subsides. Because most vapor-phase mass loss occurs relatively early in the landfill 

lifetime, it is believed to be accurate to assume a constant, average waste temperature 

representative of the active biodegradation period. 

 The model assumes that landfill gas is 50 percent methane, which is consistent with 

Bartelt-Hunt et al. (2006). Increasing this percentage would lead to less advective vapor 

mass loss. Alternative sources for this ratio were not identified. 

 Due to significant uncertainty, no attempt was made to correlate biodegradation rates 

with the waste moisture content, even though conditions with greater moisture are known 

to be more conducive to biodegradation. As an alternative, the analysis used the single, 

most conservative available value for the biodegradation rate. 

 The model does not simulate the formation of daughter products from the degradation of 

chemical constituents in the landfill. If biodegradation is significant and daughter 

products have greater toxicity than the constituent analyzed, this exclusion could lead to 

an under prediction of risk. For example, the solvents tetrachloroethylene and 

trichloroethylene can biodegrade into vinyl chloride, which has a greater toxicity than the 

parent constituents. 

 The model does not simulate water evaporation or chemical constituent volatilization 

from water within the cover or at the atmosphere/cover interface, which could lead to 

under prediction of mass loss. However, if evaporative mass loss is limited by aqueous 

diffusion, the magnitude of the under prediction would be very small (rates of aqueous 

diffusion are orders of magnitude smaller than rates of vapor diffusion). If wicking 

(upward flow of water in the cover soil driven by water evaporation and capillary forces) 

is significant, the under prediction would be somewhat greater. Incorporation of a soil 

evaporation model would enhance the model by capturing this potential chemical 

constituent release mechanism from the waste. Nevertheless, the magnitude of such 

Do not cite or quote. 3-9 
Review Draft Section 3.0 Analysis 

losses is thought to be small relative to the dominant fate and transport processes for 

solvents evaluated in this study (typically biodegradation and advective vapor transport). 

 The model assumes instantaneous and reversible equilibrium between the solid and 

aqueous phases (sorption/desorption), so that the concentration ratio between the solid 

and aqueous phases is constant (and equal to the distribution coefficient). Nonequilibrium sorption processes likely limit mass transfer between the aqueous and solid 

phases in landfills to some unknown degree. To the extent that non-equilibrium 

desorption limits mass loss from the solid phase in landfills, the LFCR may over predict 

mass loss. 

 The model adopts a linear sorption model. Nonlinear sorption typically becomes more 

significant at concentrations that are considerably higher than the constituent 

concentrations predicted at the mass loading rates of concern for wipes and laundry 

sludge disposal. Therefore, a linear sorption model was considered appropriate for this 

analysis. 

 The model considers the influence of sorption on transport through the cover/liner 

geomembrane layers. However, the model assumes that sorption is inactive within the 

cover/liner soil layers. We made this assumption because (1) we were unable to identify a 

generalized approach to estimate the cover/liner soil retardation coefficient for national 

conditions, and (2) significant uncertainties and unresolved questions remain in the 

science of transport within cover/liner soil layers. For example, sorption to mineral 

surfaces can be significant within clay rich soils; thus, the classic approach of estimating 

sorption parameters from the soil organic carbon fraction is often insufficient (Itakura et 

al., 2003). Bartelt-Hunt et al. (2003) demonstrated for engineered soils that sorption 

increases with the organic carbon fraction only up to a certain percentage (approximately 

50 percent of the cation exchange capacity). Above that carbon content, the rate of 

sorption actually decreases, likely due to a decreasing available mineral surface area. 

Another noteworthy complication involves the dependence of estimated sorption 

parameters on experimental conditions. Several researchers have found that the measured 

distribution coefficient is greater for batch versus column experiments, and the value 

decreases with the solids concentration in batch experiments (Itakura et al., 2003; Kim et 

al. 2003; Khandelwal and Rabideau, 2000). Another complexity is associated with 

diffusion and sorption behavior in unsaturated soils and a dependence on the moisture 

content (Steinberg et al., 1999). Some investigators have documented increasing rates of 

diffusive transport as the moisture content increases, likely due to decreasing availability 

of mineral surfaces for vapor sorption (Cabbar and Bostanci, 2001). In other cases, 

increasing the moisture content decreases rates of diffusion, likely due to increased 

vapor-phase tortuosity (Aubertin et al., 2000). Given the significant uncertainties and the 

absence of a generalized approach for describing cover/liner soil sorption, this analysis 

assumed no sorption within cover/liner soils. This assumption could lead to more 

conservatism, in that relatively greater constituent mass could migrate through the landfill 

cover and liner. However, in many cases, the cover/liner soil consists of an engineered 

clay primarily containing sodium bentonite with relatively little sorptive capacity (Lake 

and Rowe, 2005). Increased sorption within such engineered clays is possible if a fraction 

of the bentonite is replaced with organoclays or activated carbon. However, given the 

relatively small thicknesses of such engineered soil liners, the impact on contaminant 

migration is relatively small (Lake and Rowe, 2005). 

Do not cite or quote. 3-10 
Review Draft Section 3.0 Analysis 

 Leachate is sometimes managed (and possibly aerated) in impoundments, which could 

release volatile constituent mass to the air. This analysis did not consider this leachate 

handling process, which would require use of an additional source model describing mass 

loss from surface impoundments. Section 4 provides further evaluation of the potential 

significance of this exclusion, where it is shown that the results would not have been 

significantly changed if this mechanism had been included. 

 After collection, landfill leachate is sometimes applied to the top of active landfills. The 

LFCR model does not consider this potential mass transfer into the landfill. The potential 

significance of leachate application to the landfill surface can be evaluated by considering 

the fraction of total mass loss due to aqueous advective transport (leaching) as shown in 

Section 3.1.3 (Table 3-5). In only one case (methanol) does the average mass loss 

fraction associated with leaching exceed 10 percent of the total mass loss. The leaching 

mass loss percentage is less than 2 percent for a majority of solvents. Because many of 

these solvents would attenuate in a leachate impoundment through biodegradation, 

volatilization, and other mechanisms, the percentage contribution associated with any 

reinfiltrated leachate would be smaller. Therefore, the mass fraction reinfiltrated to the 

landfill would be less than 10 percent for methanol and much less than 10 percent for the 

other solvents. Given the relatively small percentage mass loss associated with leaching 

and the probable attenuation of mass in the impoundment for most solvents, the wipes 

and laundry sludge results would not change significantly if mass transfer associated with 

leachate reinfiltration were considered. 

3.1.2 Model Inputs 

This section provides a description of the development of input parameters supporting the 

landfill source modeling for this analysis. Appendices B, E, and F present the values and 

distributions for LFCR input parameters, along with descriptions of the basis and sources for 

values. This section provides a summary of these input data, as well as data development details 

not included in the appendices. 

3.1.2.1 Infiltration and Liner Leakage Rates 

The source model infiltration rate describes the rate of downward vertical water flow 

through the landfill, leading to aqueous advective mass transfer between daily cells, as well as 

mass loss with leachate exiting the landfill. For the no-liner case, this advective mass loss is 

transferred to the unsaturated zone. For cases with a liner, advective mass loss is recovered by a 

leachate collection system, except for the mass specified by a liner leakage rate (the flow rate 

through the liner). The average infiltration rate can change significantly once the final cover is 

installed. For example, if the final cover included a geomembrane, whereas the interim cover 

consisted of native soil, the infiltration rate would decrease significantly once the final cover was 

placed. The LFCR source model accepts as input the infiltration rate and the liner leakage rate 

before and after the final cover is installed. 

Values for infiltration and liner leakage rates were taken from the existing EPACMTP 

regional, site-based database (U.S. EPA, 2003a). The EPACMTP database was originally 

developed to represent national variability for use in Monte Carlo risk analyses. The EPACMTP 

database provides “regional, site-based” parameter values, whereby physically unrealistic 

parameter combinations are avoided in the representation of national variability. For example, 

the database would not combine a shallow depth to groundwater similar to conditions in Florida 

Do not cite or quote. 3-11 
Review Draft Section 3.0 Analysis 

with an arid-climate recharge rate similar to conditions in the southwestern United States. 

Infiltration rates within EPACMTP were developed from Hydrologic Evaluation of Landfill 

Performance (HELP) simulations and empirical data. The EPACMTP system has been peer 

reviewed and used for several risk analyses over the past 15 years. The EPACMTP Technical 

Background Document (U.S. EPA, 2003c,d) and Parameters and Data Background Document

(U.S. EPA, 2003a) provide details about the development of EPACMTP. The reader is referred 

to these documents for additional background information. Further details about EPACMTP are 

provided below only in cases where the data development diverges from these documents. Note 

that the EPACMTP groundwater transport model was used in this analysis (see Section 3.3). 

Input values for subsurface modeling parameters common to both the LFCR and EPACMTP 

were shared, thereby providing consistency with the EPACMTP regional site-based framework. 

Values for infiltration and liner leakage rates correspond to the EPACMTP database 

infiltration rate parameter SINFIL (U.S. EPA, 2003a). Two sets of SINFIL values are available 

in EPACMTP to represent conditions with (1) a soil-only cover and no liner, and (2) a composite 

cover and liner. EPACMTP no-liner infiltration rates were derived from HELP simulations that 

considered climatic and site-specific variability (e.g., rainfall and soil type) and a soil-only cover 

(U.S. EPA, 2003a). EPACMTP infiltration rates representative of a closed landfill with a 

composite liner were derived from empirical liner leakage data. 

For this analysis, a modification was made to the empirical liner leakage rate distribution 

relative to the approach documented in U.S. EPA (2003a). The liner leakage rates were based on 

monthly average leak detection system (LDS) flow rates as reported by TetraTech (2001). The 

infiltration data in the TetraTech report cover five general liner configurations: 

 GM: A high-density polyethylene geomembrane over native soil 

 GM/CCL: A 1- to 1.5-mm thick geomembrane overlying a 3-ft thick compacted clay 

liner 

 GM/GCL: A 1- to 1.5-mm thick geomembrane overlying a 6-mm thick geosynthetic clay 

liner 

 GM/GCL/GM: A 6-mm thick geosynthetic clay liner between two 1- to 1.5-mm thick 

geomembranes 

 GM/GCL/CCL: A 1- to 1.5-mm thick geomembrane overlying a 6-mm thick geosynthetic 

clay liner and a 3-ft thick compacted clay liner. 

Due to the potential for water to be released during the consolidation of a compacted clay 

liner (yielding an unknown contribution of water to the LDS flow rate), the compacted clay liner 

leakage data (GM/CCL and GM/GCL) originally were not used (U.S.EPA, 2003a). However, 

release of water during consolidation would likely be less significant at higher infiltration rates, 

which would correspond to the upper end of the risk distribution. For this reason, EPA chose to 

retain the compacted clay liner data in developing the liner leakage distributions for this analysis. 

However, the geomembrane (GM) data were excluded from the leakage rate distribution, 

because the geomembrane liner type is inconsistent with requirements for newly constructed 

landfills. 

Inclusion of compacted clay liner data and removal of geomembrane liner data resulted in 

an infiltration rate distribution based on 53 data points. These data were further screened to 

exclude data points from monitoring studies less than 6 months long, as directed by EPA. Six 

Do not cite or quote. 3-12 
Review Draft Section 3.0 Analysis 

months was selected as a reasonable screening criterion above which the results of monitoring 

studies would not be expected to be uniformly influenced by seasonal variations (and hence 

would not unduly affect the distribution of LDS flow rates). After applying the study duration 

criterion, the final data set consisted of 50 data points used to define the composite-liner leakage 

distribution. Summary statistics for that distribution are presented in Appendix F (Table F-3). 

Before the final cover is installed, landfills are often covered temporarily with soil. 

Because the EPACMTP no-liner infiltration rates represent landfills with a soil-only cover, these 

rates were used for conditions before the final cover in both the lined and unlined landfill 

scenarios. After installation of the final cover, the EPACMTP composite-liner rates were used 

for the lined landfill scenario, and the EPACMTP no-liner infiltration rates were used for the 

unlined landfill scenario. Landfill leakage rates in the source model corresponded to the 

EPACMTP composite liner rates throughout the simulation. In the unlined scenario, liner 

leakage rates were not used, because all of the leachate flowing through the landfill migrates to 

the underlying groundwater. In 14 percent of the 10,000 realizations, SINFIL (composite liner) 

exceeded SINFIL (no liner) in the EPACMTP database. To maintain data consistency with the 

groundwater modeling simulations for the lined landfill scenario, the infiltration rate before the 

final cover was set to SINFIL (composite liner) rather than SINFIL (no liner) for those 

realizations. 

3.1.2.2 Cover/Liner Characterization Parameters 

The LFCR model describes diffusive transport through various potential cover materials, 

including daily, intermediate, final soil, and final composite (soil and geomembrane) covers. 

This analysis considered two liner scenarios: a composite-liner scenario and a no-liner scenario. 

Each of the cover and liner types requires the following input parameters to describe diffusive 

transport through the respective layers: soil porosity, moisture content, and thickness. Diffusion 

through a geomembrane also requires specification of a permeation coefficient, which is 

tabulated and described in Appendices B and C. The permeation coefficient captures the effects 

of geomembrane sorption and diffusion. Sorption to the cover/liner soil layers was assumed to be 

negligible (see Section 3.1.1.2). 

Cover and liner thicknesses were consistent with values specified in Subtitle D 

regulations (e.g., a liner 2 feet thick). Soil parameter values (moisture content, porosity) were 

from the EPACMTP database, which includes a distribution of three soil types designed to 

represent national variability (U.S. EPA, 2003a). Table 3-1 presents the effective porosity and 

field capacity values associated with these EPACMTP soil types. The porosity and moisture 

content for the daily, intermediate, and final cover (no-liner scenario, soil-only cover) were set to 

the effective porosity and field capacity associated with the EPACMTP soil type. The field 

capacity was considered a reasonable estimate of long-term average moisture conditions for 

these soils (Bartelt-Hunt, 2006). The porosity for the clay layer within a composite cover and 

liner was set to the porosity of liner soil in the HELP documentation (Schroeder et al., 1994). 

The moisture content for the clay layer within a composite cover and liner was equal to the 

average of liner moisture contents reported in Benson et al. (1999). 

Do not cite or quote. 3-13 
Review Draft Section 3.0 Analysis 

Table 3-1. Characteristics of EPACMTP Soils 

Soil Type 

EPACMTP 

Soil ID 

HELP Soil 

Number 

Total 

Porosity 

Effective 

Porosity 

Field 

Capacity 

Wilting 

Point 

Saturated 

Hydraulic 

Conductivity 

(cm/sec) 

Sandy Loam 2 6 0.453 0.41 0.19 0.085 0.00072 

Silty Loam 1 9 0.501 0.45 0.284 0.135 0.00019 

Silty Clay Loam 3 12 0.471 0.43 0.342 0.21 0.000042 

Porosity and moisture content values expressed as volume fractions (volume void/water divided by total volume). 

3.1.2.3 Hydrogeologic Setting Parameters 

The EPACMTP database provided hydrogeologic setting parameter values, including the 

depth to groundwater (EPACMTP parameter DSOIL, used for the unsaturated zone thickness) 

and the EPACMTP regional soil type. The EPACMTP regional soil type comprises the three 

soils listed in Table 3-1 in a distribution representative of national conditions (U.S. EPA, 2003a). 

The unsaturated zone porosity and moisture content were equal to values consistent with the 

EPACMTP regional soil type. 

3.1.2.4 Landfill Characterization Parameters 

Each landfill simulation includes a series of sequential landfill cells. The landfill cell 

operating life (time period of waste application for one landfill cell) of 3 to 5 years is consistent 

with typical industry practice (Barlaz, 2006). The total landfill operating life values were 

adjusted to the nearest integer evenly divisible by the landfill cell operating life, so that a whole 

number of landfill cells make up a given landfill (i.e., no partial cells). The time period before a 

final cover is installed is also consistent with typical industry practice in that there is typically a 

delay before landfill managers install the final cover (Barlaz, 2006). The daily cell/lift height 

describes the thickness of the active waste layer and is derived from Aivaliotis et al. (2004) and 

typical industry practice (Barlaz, 2006). 

The fraction of day without a cover describes the time when waste is being added to the 

active daily cell before the daily cover is applied. Typical industry practice for this stage is 

8 hours (Barlaz, 2006). The daily waste sublayer period describes the time period during which a 

sublayer of waste is added to a daily cell. The no-cover mass loss formulation (see Appendix C) 

uses this parameter to describe mass loss from layers as waste is added throughout the workday. 

A value of 1 hour was selected based on professional judgment (Barlaz, 2006); however, model 

testing did not indicate that the model was very sensitive to this parameter. The no-cover mass 

loss formulation also uses the angle of the working face to the horizontal. Aivaliotis et al. (2004) 

provided a distribution of values for this angle, although testing results showed that the model 

was also not very sensitive to this parameter. 

Several EPA-sponsored modeling analyses have focused on smaller landfills associated 

with an Industrial D database (U.S. EPA, 2003a,b,c). For the current analysis, a more recent 

database that EPA obtained commercially from the Waste Business Journal (WBJ) and analyzed 

(referred to here as the EPA-WBJ database) was considered a more representative data source for 

municipal landfill geometry and operating life characteristics. Of special significance given the 

design architecture of the LFCR model was the requirement that average landfill depths be 

realistic and consistent with known operating practices (e.g., lift heights). However, due to 

copyright concerns, EPA was only able to provide summary indicators from the EPA-WBJ data 

Do not cite or quote. 3-14 
Review Draft Section 3.0 Analysis 

(rather than data on specific landfills directly from the original WBJ database). This created 

some challenges in constructing a data set that could be used by the model, as discussed further 

below. 

EPA’s analysis of the WBJ database suggested that the landfill parameters could be 

described with lognormal distributions. Therefore, EPA provided the associated parametric 

statistics for the following variables contained in the database: area (m2), operating schedule 

(days/yr), and daily intake rate (tons/day). EPA also derived a number of variables from the 

reported data and calculated lognormal parametric statistics for these variables, as follows: 

operating life (yrs), total capacity (tons), total landfill volume (m3

), and landfill depth (m). 

Unless start and closure dates were both reported, the operating life was derived assuming the 

reported operating schedule and daily intake rate throughout the life of the landfill. The derived 

total capacity assumed the same operating schedule and waste intake rate from the reported start 

date to the present (and then added the remaining capacity). The derived landfill volume 

assumed a compacted wet waste density of 1,160 lbs/yd3. Average landfill depth was simply the 

volume divided by the area. EPA also developed several uni- and multivariate parameter 

correlations, including descriptions of the relationship between capacity and intake, area and 

intake, area and capacity, area and depth, area and operating life, and intake and operating life. 

Notably, there was no significant correlation between landfill depth and landfill area. 

The statistical information described above was used to derive preliminary combinations 

of landfill characteristics. However, several initial parameter values and combinations were 

unrealistic and, in many cases (more than 10 percent), inconsistent with the summary statistics 

from the EPA-WBJ database. For instance, the resulting range of landfill depths (0.0026–

1,412 m) was significantly outside of the range of depths in the EPA-WBJ database (0.5–115 m). 

Similarly, the landfill operating life distribution contained unrealistic values (e.g., thousands of 

years). Consequently, an alternative approach was developed that could 

 Create a representative set of landfills that were realistic from an engineering standpoint 

(i.e., parameter values were calculated as appropriate from key landfill variables) 

 Avoid producing any parameter values outside the realistic range defined by the 

EPA-WBJ data 

 Provide positive correlations for parameters shown to be positively correlated in the 

EPA-WBJ database (e.g., area and capacity) 

 Allow for comparison with other data sources, recognizing that some data sources focus 

on specific subsets of MSWLFs 

 Reflect the operating assumptions (minimum height) associated with the landfill source 

model. 

The approach was initiated by using the statistical descriptors for parametric distributions 

(assumed to be lognormal) provided by EPA to create distributions of 15,000 values for three 

basic landfill parameters: 

 Area (as reported in the EPA-WBJ database) 

 Depth (derived from EPA-WBJ database) 

 Operating life (derived from the EPA-WBJ database). 

Do not cite or quote. 3-15 
Review Draft Section 3.0 Analysis 

To accommodate height requirements of the landfill source model (2-ft final cover, 2-ft 

liner, 6-inch daily cover, and a minimum lift height of 4 ft), the minimum landfill depth was set 

to 2.9 m, which was approximately the 25th percentile in the distribution created using the 

EPA-WBJ database.14 This minimum shifted the landfill depth distribution upward, which was 

more consistent with other data sources and expert recommendations (e.g., EREF, 1999; Barlaz, 

2006). Using the distributions for landfill area (m2

), landfill depth (m), and operating life (yr), we 

calculated values for three other parameters as follows: 

 Landfill volume (m3

) = landfill area (m2) x landfill depth (m) 

 Landfill capacity (tons) = landfill volume (m3

) x wet density of the waste (ton/m3) 

 Daily waste intake rate (tons/d) = landfill capacity (tons) x operating life (yr)/operating 

days/yr. 

The wet density of the waste was assumed to be 0.801 ton/m3 (Barlaz, 2006). A 6-dayper-week (312 days per year) operating schedule was assumed (the predominant operating 

schedule in the EPA-WBJ database). Parameter distributions of 15,000 landfills were created. 

Values outside of the EPA-WBJ minima and maxima were excluded, resulting in approximately 

1 percent (201 of the 15,000) being eliminated. Next, correlations between parameters were 

compared with correlations from the EPA-WBJ data. For example, the correlation coefficient 

between the daily waste intake rate and the capacity in the constructed data set was 0.9, as 

compared with a correlation coefficient of 0.93 in the EPA-WBJ database. Similarly, the 

correlation coefficient between area and capacity was 0.6 for the constructed landfill data set and 

0.53 for the EPA-WBJ database. Thus, the strength of the correlations between critical landfill 

parameters was very similar in the constructed landfill data set and the EPA-WBJ database. 

Finally, basic statistics (average, minimum, maximum) from the constructed data set were 

compared with those from other sources (EREF, 1999; Themelis and Ulloa, 2005; U.S. EPA, 

2006a), to take into account the fact that some of the other sources focus on different subsets of 

landfills. For instance, EPA’s Landfill Methane Outreach Program (LMOP) database (U.S. EPA, 

2006a) and the Themelis and Ulloa (2005) data focus on MSWLFs having landfill gas energy 

potential, and thus tend to include larger landfills. 

The comparison shown in Table 3-2 suggested that the constructed landfill data set tends 

to have somewhat larger areas and somewhat shallower depths when compared with other recent 

data sources. In addition, relative to the EPA-WJB database, the constructed data set generally 

shows smaller variability in the parameter distributions. These differences notwithstanding, the 

similarity in correlations, value ranges, and averages strongly suggests that the constructed data 

set achieves all five goals described above and provides reasonable representations of MSWLFs 

for use with the LFCR model. Appendix F (Table F-3) provides the cumulative distribution 

functions (CDFs) for the constructed data set parameters. 

 

14 Note that the preferred length unit for this analysis was meters; however, measurements are sometimes presented 

in English units, given the common usage of this measuring system in source data. 

Do not cite or quote. 3-16 
Review Draft Section 3.0 Analysis 

Table 3-2. Comparison of Constructed Data Set with Other Sources 

Statistic EPA-WBJ 

Wipes 

Constructed 

Data Set EREF (1999) 

Themelis and 

Uloa (2005) 

EPACMTP 

(U.S. EPA, 

2003a) LMOP 

Area (m2) 

Avg 848,784 770,464 566,856 5,674,167 522,521 512,824 

Max 24,281,238 23,849,771 1,809,009 57,460,000 9,342,000 8,781,990 

Min 4,047 4,072 141,645 170,000 4,010 4,047 

SD 1,441,143 1,280,745 380,466 - - 705,339 

N 1,639 14,799 44 36 - 1,046 

Depth (m) 

Avg 10.6 18.2 24.1 28 3.6 28.9 

Max 115 114.8 48.9 53 10.1 132.7 

Min 0.5 2.9 8.5 14 0.6 2.4 

SD 15.7 19.2 8.9 - - 20.2 

N - 14,799 44.0 36 - 468.0 

Volume (m3) 

Avg 10,096,000 12,062,539 13,646,339 158,719,051 1,894,461 14,843,695 

Max 484,218,056 305,127,109 88,462,272 3,045,380,000 93,980,520 1,165,150,523 

Min 124 22,093 1,204,875 2,380,000 2,446 9,875 

SD 26,124,739 21,894,169 - - - - 

N 1,829 14,799 - - - - 

Capacity (tons) 

Avg 7,658,940 9,656,343 10,924,211 127,058,285 1,516,560 11,882,722 

Max 367,333,317 244,261,335 70,816,103 2,437,897,397 75,233,588 932,730,046 

Min 94 17,686 964,530 1,905,245 1,958 7,905 

SD 19,818,524 17,526,790 - - - - 

N 1,829 14,799 - - - - 

Active life (yr) 

Avg 44 42.2 - - - - 

Max 200 194.2 - - - - 

Min 3.9 7.6 - - - - 

SD 20 18 - - - - 

N - 14,799 - - - - 

Daily Waste Intake (tons/day) 

Avg 457 832 - - - - 

Max 13,014 11216 - - - - 

Min 0.01 0.90 - - - - 

SD 1,013 1472 - - - - 

N 2,411 14,799 - - - - 

Do not cite or quote. 3-17 
Review Draft Section 3.0 Analysis 

3.1.2.5 Simulation Parameters 

Several parameters control the model simulation, including the maximum allowed 

simulation time and error tolerances. Appendix E provides values and descriptions for these 

parameters. There is a balance between the Runge-Kutte truncation error tolerance, the solution 

mass balance, and runtime, whereby the mass balance accuracy and runtime decrease as the error 

tolerance increases. The error tolerance was set so that the mass balance error was maintained 

below 1 percent for all robustness-testing simulations. 

3.1.2.6 Waste Characterization Parameters 

A series of parameters characterizing the landfill waste appears in Appendix E. The 

landfill waste temperature is generally elevated as a result of biodegradation. The average waste 

temperature is used to adjust diffusion coefficients and Henry’s law constants for temperature 

effects. The model accepts a single, average temperature, although the temperature in landfills 

does decrease over time as biodegradation subsides. Nevertheless, this approach was chosen 

because of the unavailability of simple models to describe temperature fluctuations over time. 

Also, volatile solvents are often released relatively early in a landfill’s life, when biodegradation 

is active and temperatures are generally elevated. 

The distributions for waste bulk density and volumetric moisture content were calculated 

based on values of wet density and the gravimetric moisture content typical of municipal waste, 

assuming a water density of 1,000 kg/m3 (Barlaz, 2006). The waste moisture content distribution 

was correlated with the infiltration rate with a correlation coefficient of 0.85 to reflect the 

influence of climatic variation on moisture. The waste porosity distribution is typical for 

municipal waste and was based on Qian et al. (2002) and Barlaz (2006). The porosity was 

correlated inversely with bulk density (correlation coefficient of -0.75) to reflect that the bulk 

density tends to increase as the porosity decreases. The distribution for waste organic carbon 

fraction is typical for municipal waste and was based on Bartelt-Hunt et al. (2006) and Barlaz 

(2006). 

The solvent concentration in the waste was set to a constant value of 1 kg/m3 for the 

landfill source model simulations. Note that this is a total volumetric concentration including the 

mass in the solid, aqueous, and vapor phases (see Appendix C for details). Because the analysis 

results were expressed in terms of mass loading limits rather than concentrations, the results 

were scaled linearly to maintain a constant mass loading rate (see Section 5). The model linearity 

allows for this straightforward adjustment of the results. 

The LFCR model uses an exponential decay model similar to LandGEM to describe 

landfill gas generation (U.S. EPA, 2003a). The controlling parameters are the gas generation rate 

and the potential methane generation capacity. The gas generation rate is known to vary with 

moisture conditions, because biological activity generally increases with moisture. Therefore, a 

relationship was developed between the infiltration rate and the gas generation rate based on 

Barlaz (2006), Bartelt-Hunt et al. (2006), and the AP-42 user’s manual (U.S. EPA, 1997a). The 

resulting piecewise linear function provides a method for calculating the gas generation rate from 

the infiltration rate as presented in Table 3-3 and Figure 3-4. The potential methane generation 

rate distribution was based on the AP-42 user’s manual (U.S. EPA, 1997a) and Barlaz (2006). 

The model assumes that landfill gas is made up of 50 percent methane, which is consistent with 

Bartelt-Hunt et al. (2006). 

Do not cite or quote. 3-18 
Review Draft Section 3.0 Analysis 

Table 3-3. Relationship Between the Infiltration Rate and the Gas Generation Rate 

Infiltration 

Rate (m/yr) 

Min Max 

Gas Generation Rate 

(/yr) Basis 

< 0.02 = 0.01 Minimum value of 0.01 (Barlaz, 2006) 

0.02 0.04 Infiltration / 2 AP-42 arid climate rate (U.S. EPA, 1997a), Bartelt-Hunt et al. 

(2006) 

0.04 0.12 0.25 x Infiltration + 0.01 AP-42 wet climate rate (U.S. EPA, 1997a), Bartelt-Hunt et al. 

(2006) 

0.12 0.36 0.25 x Infiltration + 0.01 Linear relationship (Barlaz, 2006) 

> 0.36 = 0.1 Maximum value of 0.1 (Barlaz, 2006) 

0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Infiltration Rate (m/yr)

Gas Generation Rate (/yr)

Figure 3-4. Relationship between the infiltration rate and the gas generation rate. 

3.1.2.7 Chemical-Specific Parameters 

Values for a series of chemical-specific parameters used by the landfill source model 

appear in Appendix B. Several of these parameters are basic chemical characteristics such as 

Henry’s law constant, organic carbon distribution coefficient, molar volume, molecular weight, 

boiling point, critical temperature, and critical pressure. Standard references such as the 

Superfund Chemical Data Matrix (SCDM) and the Hazardous Substances Data Bank (HSDB) 

provided values for these chemical characteristics (U.S. EPA, 2002b; NLM, 2006). 

The LFCR model provides the capability of adjusting Henry’s constants and diffusivities 

to reflect their dependence on temperature. As described in Appendix B, the Fuller, Schettler, 

Giddings method was used to adjust diffusivities in air; the Hayduk and Laudie method was used 

Do not cite or quote. 3-19 
Review Draft Section 3.0 Analysis 

to adjust diffusivities in water; and the Clausius-Clapeyron method was used to adjust Henry’s 

constants (Fuller et al., 1966; Lyman, 1990; Perry and Green, 1997). 

Values for the permeation coefficient describing geomembrane sorption and diffusion 

processes were derived either from laboratory values reported in the literature or from an 

empirical correlation presented in Sangam and Rowe (2001). This correlation relates the 

permeation coefficient to the octanol-water partition coefficient. Appendix B provides this 

correlation and further details about the permeation coefficient input values. 

Hydrolysis rates (abiotic degradation rates) were taken from U.S. EPA (1993b) and NLM 

(2006). A uniform distribution ranging over two orders of magnitude was used to represent the 

variability and uncertainty. However, the influence of abiotic degradation was negligible relative 

to biodegradation, because the abiotic degradation rates were orders of magnitude lower than 

biodegradation rates. 

Values for biodegradation half-lives were collected from several sources, including 

Howard et al. (1991), Aronson and Howard (1997), and U.S. EPA (1999b). Because landfill 

waste primarily degrades under anoxic conditions, reported anaerobic degradation rates were 

used. Initially, ranges of half-life values were assembled to represent variable conditions. 

However, based on peer review comments and discussions with EPA, the most conservative 

values within the ranges were selected for all simulations (the maximum half-lives/minimum 

biodegradation rates). It should be noted that the biodegradation rate values generally were not 

developed for landfills. Rather, the secondary data sources reflect the broader environmental 

science literature. Therefore, significant uncertainty remains in the biodegradation rate estimates. 

Relatively little information was identified in the literature characterizing field-scale 

biodegradation rates in landfills. The analysis also did not consider the known correlation 

between moisture content and biological activity. However, using the maximum available halflife value achieved EPA’s goal of greater conservatism in the biodegradation estimates. 

3.1.3 Source Model Outputs 

With two liner scenarios, 20 solvents, and 10,000 realizations per liner and solvent 

combination, the analysis included 400,000 LFCR source model simulations. Table 3-4 provides 

process summary results from the source modeling simulations. The average percent mass 

balance error of 0.5 percent was maintained below the target 1 percent. A relatively small 

percentage of runs (15 percent) exceeded this 1 percent target; however the maximum mass 

balance error was only 1.4 percent for one simulation. The average simulation time was 0.05 

minutes, with a total runtime for all 400,000 simulations of 22,040 minutes, or approximately 

15 days. The simulations were performed in parallel on multiple processors, so that the actual 

time to complete the simulations was a few days. Table 3-4 also provides average, maximum, 

and minimum landfill characteristics, including the number of lifts, columns, and daily cells, as 

well as the daily cell volume, height, and width. The number of columns and daily cell volume 

for the composite and no-liner scenarios differ slightly due to the fact that the liner occupies 

some of the landfill volume in the composite liner scenario. 

Do not cite or quote. 3-20 
Review Draft Section 3.0 Analysis 

Table 3-4. Process Summary Results for All Solvents 

Composite-Liner Scenario No-Liner Scenario 

Result Avg Max Min Avg Max Min 

Percent mass balance error (%) 0.5 1.4 4.3E-05 0.5 1.4 1.0E-05 

Simulation runtime (minutes) 0.05 0.13 0.02 0.06 0.1 0.0166667 

Number of lifts 6.4 92 2 6.5 92 2 

Number of columns (in one landfill cell) 632 1,764 16 556 1,600 16 

Number of daily cells (in one landfill cell) 1,454 1,825 1,095 1,454 1,825 1,095 

Daily cell volume (m3) 821 12,304 0.6 856 12,428 0.8 

Daily cell/lift height (m) 3.3 6.5 1.0 3.3 6.5 1.0 

Daily cell width (m) 13 98 0.6 13 98 0.6 

In response to peer review comments on the LFCR model, we developed several types of 

analyses shown in Tables 3-5 through 3-7 to characterize the behavior of solvents in the landfill, 

focusing on key processes that control landfill mass losses. These analyses were necessary 

because the LFCR model was developed specifically for this analysis and represents a significant 

step forward in simulating landfill releases for a national-scale analysis. Each of the tables 

presents percent losses of solvent mass associated with specific processes for the no-liner and 

composite-liner scenarios, respectively, and shows the percent loss associated with the 90th 

percentile risk, the minimum percent loss, and the maximum percent loss. Table 3-5 presents 

results for all primary fate and transport pathways, including 

 Diffusion of solvents from the bottom of the landfill 

 Aqueous advection (leaching) of solvents with water leaving the bottom of the landfill (in 

the composite-liner scenario, this mass is recovered by a leachate collection system 

except for the fraction associated with liner leakage; for the no-liner scenario, this mass 

reaches the unsaturated zone) 

 Vapor advection of solvents with landfill gas flowing from the landfill into the air 

 Diffusion of solvents from the landfill into the air (includes diffusion with and without 

cover) 

 Degradation of aqueous phase solvents (e.g., biodegradation). 

The percent contributions across these primary loss mechanisms and the mass balance 

error sum to 100 percent, indicating that these processes account for 100 percent of the solvent 

mass that is “lost” to the landfill. The first three rows of Table 3-5 present the averages across all 

solvents for the no-liner scenario, the composite-liner scenario, and both scenarios combined. As 

shown in row three of this table, the largest average loss across both liner types is due to aqueous 

degradation (38 percent), followed by the advective losses to air (19 percent), diffusive losses to 

air (10 percent), diffusive losses to the unsaturated zone (9 percent), and aqueous advective 

losses (7 percent). Relatively speaking, these percent contributions are consistent with the 

simulation results from the MOCLA model, indicating that (1) degradation can be a dominant 

Do not cite or quote. 3-21 
Review Draft Section 3.0 Analysis 

Do not cite or quote. 3-22 

loss mechanism, and (2) advection of landfill gases can be significant for volatile constituents 

(Kjeldsen et al., 2002). 

In addition, Table 3-5 also shows the percent of solvent mass still remaining in the 

landfill at the end of the maximum landfill simulation time (i.e., 200 years). Note that the 

percentage remaining is significant (greater than 60 percent at the 90th percentile risk) for some 

of the more recalcitrant solvents, such as cresol; 1,1,2-trichloroethane; and xylene. The mass loss 

rates to air and groundwater generally reach their maximum levels around the end of the landfill 

operating life, with the maximum fluxes to air and groundwater occurring on average around 

year 40 of the simulation. Because the mass loss rates at the end of the simulation are typically 

very low, extending the landfill simulation time beyond the 200 year maximum and thus 

decreasing the percent mass remaining would not result in a substantial change in the mass losses 

and flux estimates required by the groundwater and air pathway models. 

Table 3-6 provides the total percentages represented by mass transfer to the atmosphere 

and to the unsaturated zone (inputs to the groundwater and air transport models). The total mass 

loss to the atmosphere is the sum of contributions due to vapor diffusion and vapor advection. 

The total mass loss to groundwater is the sum of contributions from aqueous diffusion and 

leachate (for the no-liner scenario) or liner leakage (for the composite-liner scenario). Table 3-7

gives the diffusive no-cover mass loss that occurs while the daily waste is uncovered (a fraction 

of the total vapor diffusive losses in Table 3-5), as well as liner leakage mass loss (a fraction of 

the aqueous advective mass loss in Table 3-5). Mass loss during active waste addition (with no 

cover) was relatively less significant, with an average mass loss of 3 percent (row three in 

Table 3-7). In selected cases, the no-cover mass loss was more significant (maximum percentage 

loss of 75 percent for a single run), but in only 3 percent of the cases was the no-cover mass loss 

percentage greater than 10 percent. Although the rate of no-cover loss is typically dominant for 

the active daily cell, this daily cell accounts for a small volume of waste and a small time 

increment relative to the entire landfill. 

In the no-liner scenario, all aqueous advection losses reach the subsurface below the 

landfill. In the composite-liner scenario, aqueous advection losses are recovered in a leachate 

collection system, with the exception of the liner leakage. 

The results differ significantly among the different solvents, varying predictably with 

respect to differences in chemical properties. For example, the more volatile solvents (e.g., 

tetrachloroethylene, benzene) exhibit greater advective losses in the vapor phase. The least 

volatile solvents (e.g., ethyl acetate, butanol) are primarily lost through degradation, because 

they generally have greater residence time in the landfill and are thus more available for 

degradation. The mass loss to groundwater is consistently greater in the no-liner versus the 

composite-liner scenario, including the total loss to groundwater (Table 3-6) and diffusion to the 

unsaturated zone (Table 3-5). 
Review Draft 

Table 3-5. Percent Contributions of Primary Loss Mechanisms in the Landfill 

Diffusion to 

Subsurface 

Aqueous 

Advection 

Vapor 

Advection 

Diffusion to 

Atmosphere Aqueous Degradation

Mass Rema

Landfill at 2

Liner 90th Range 90th Range 90th Range 90th Range 90th Range 90th R

None 15.29 0 - 74.16 11.17 0 - 91.05 18.17 0 - 96.1 9.02 0.01 - 80.92 33.89 0.01 - 98.92 11.98 0.1 

All solvents Composite 2.28 0 - 26.44 2.32 0 - 87.59 19.89 0 - 96.23 11.63 0.01 - 95.42 41.24 0.02 - 98.94 22.19 0.1 

Both 8.79 0 - 74.16 6.74 0 - 91.05 19.03 0 - 96.23 10.32 0.01 - 95.42 37.56 0.01 - 98.94 17.09 0.1 

Solvent-Specific Percent Contributions 

None 1.7 0.04 - 37.73 8.6 0 - 39.97 10.1 0.85 - 21.93 4.1 0.44 - 43.36 75.1 34.71 - 95.61 0.1 0.

Acetone 

Composite 0.02 0 - 0.07 9.8 0 - 48.61 4.9 0.9 - 21.92 3.2 0.44 - 48.79 81.5 34.83 - 96.07 0.1 0.

None 15.4 0.55 - 72.01 0.2 0 - 5.59 58.1 2.48 - 94.16 24.4 1.28 - 70.92 1.5 0.15 - 15.22 0.1 0.1 

Benzene 

Composite 1.4 0.08 - 15.86 0.8 0 - 8.03 54.8 3.45 - 94.23 37.5 1.2 - 95.42 3.9 0.26 - 16.59 1.6 0.1 

None 0.7 0 - 7.42 6.9 0 - 20.95 0.2 0.06 - 0.54 0.7 0.03 - 3.76 90.1 75.96 - 98.18 0.1 0.1 

Butanol 

Composite 0.005 0 - 0.21 0.02 0 - 18.44 0.1 0.06 - 0.55 0.1 0.03 - 3.97 97.8 79.54 - 98.23 0.6 0.1 

None 34.8 1.24 - 73.73 1.5 0 - 11.58 43.5 3.25 - 70.84 8.7 0.79 - 63 4.5 0.31 - 18.19 6.4 0.1 

Chlorobenzene 

Composite 7.2 0.4 - 26.44 0.6 0 - 7.71 32.3 7.07 - 70.92 22.7 1.03 - 86.05 11.1 0.84 - 19.13 25.8 0.24 

None 1.0 0.01 - 8.18 27.9 0 - 91.05 0.1 0.03 - 0.21 0.1 0.01 - 1.99 19.0 6.92 - 37.03 51.8 0.1 

Cresols (total) 

Composite 0.4 0.02 - 2.15 9.0 0 - 52.25 0.1 0.03 - 0.22 0.2 0.01 - 1.95 26.1 15.19 - 38.58 64.0 28.1

None 2.6 0.06 - 49.37 80.0 0 - 82.51 1.5 0.53 - 2.87 1.6 0.06 - 13.22 13.3 11.15 - 76.58 0.1 0.1 

Cyclohexanone 

Composite 0.3 0.02 - 2.71 0.7 0 - 72.89 1.6 0.55 - 3.02 0.8 0.05 - 8.94 52.6 20.96 - 80.69 43.7 2.93 

Dichloro- None 27.3 0.73 - 72.68 0.4 0 - 14.74 19.2 3.78 - 35.75 12.6 0.37 - 55.34 6.7 0.58 - 14.35 33.6 0.1 benzene, 1,2- Composite 1.5 0.16 - 16.87 0.1 0 - 7.41 20.2 7.67 - 35.86 3.1 0.44 - 62.68 8.9 2.92 - 15.29 66.2 11.8

None 0.01 0 - 0.27 0.05 0 - 36.59 0.005 0 - 0.03 0.1 0.01 - 0.85 98.4 62.28 - 98.57 0.1 0.

2-Ethoxy-ethanol 

Composite 0.01 0 - 0.09 1.4 0 - 39.63 0.01 0 - 0.03 0.1 0.01 - 0.94 97.2 59.37 - 98.58 0.1 0.

None 9.4 0.02 - 27.73 3.1 0 - 10.48 2.2 0.49 - 5.56 1.3 0.11 - 14.97 82.7 63.88 - 97.49 0.1 0.

Ethyl acetate 

Composite 0.02 0 - 0.11 1.3 0 - 12.26 2.0 0.5 - 5.58 1.5 0.11 - 16.46 93.8 73.06 - 97.65 0.1 0.

None 32.8 1.16 - 74.13 1.1 0 - 5.8 34.0 3.7 - 57.97 7.9 0.63 - 61.88 1.7 0.09 - 4.05 22.1 0.1 

Ethyl benzene 

Composite 14.2 0.34 - 23.72 0.6 0 - 3.19 24.0 9.31 - 57.95 20.3 0.8 - 79.92 2.2 0.34 - 4.34 38.3 2.3 

None 0.1 0 - 5.22 0.8 0 - 12.35 2.3 0.07 - 3.57 2.1 0.14 - 17.33 94.4 78.49 - 98.92 0.1 0.

Isobutanol 

Composite 0.02 0 - 0.11 3.2 0 - 15.69 0.5 0.06 - 3.57 1.0 0.14 - 19.16 94.9 78.3 - 98.94 0.1 0.

Do not cite or quote. 
Review Draft 

Do not cite or quote. 

Table 3-5. (continued) 

Diffusion to 

Unsaturated Zone 

Aqueous 

Advection 

Vapor 

Advection 

Diffusion to 

Atmosphere Aqueous Degradation

Mass Rema

Landfill at 2

Liner 90th Range 90th Range 90th Range 90th Range 90th Range 90th R

Solvent-Specific Percent Contributions (continued) 

None 5.2 0.12 - 61.5 77.6 0 - 89.48 0.8 0.36 - 2.04 0.5 0.08 - 18.96 15.5 5.14 - 63.98 0.1 0.1 

Methanol 

Composite 0.02 0 - 0.04 14.4 0 - 87.59 1.6 0.38 - 2.23 2.6 0.07 - 11.63 46.5 8.24 - 71.16 34.5 1.51 

Methylene None 19.0 0.26 - 63.83 1.5 0 - 5.43 27.0 1.77 - 89.26 41.2 1.78 - 80.92 11.2 1.32 - 62.3 0.1 0.chloride Composite 0.1 0.01 - 1.16 0.5 0 - 11.5 53.3 2.19 - 89.39 28.2 1.79 - 94.72 17.7 2.52 - 65.81 0.1 0.Methyl ethyl None 0.9 0.03 - 35.33 8.1 0 - 31.19 9.8 0.9 - 29.15 12.1 0.51 - 48.19 68.9 33.22 - 95.24 0.1 0.ketone Composite 0.01 0 - 0.3 1.4 0 - 39.36 10.0 0.92 - 29.14 1.7 0.51 - 53.7 86.4 39.62 - 95.9 0.1 0.Methyl isobutyl None 3.4 0.02 - 25.82 3.5 0 - 10.05 6.3 0.64 - 18.72 4.6 0.31 - 33.64 81.6 52.05 - 96.93 0.1 0.ketone Composite 0.002 0 - 0.16 0.1 0 - 13.82 9.6 0.61 - 18.72 2.1 0.31 - 37.48 87.6 58.85 - 97.36 0.1 0.

None 18.0 1.34 - 74.16 0.1 0 - 7.45 39.3 3.22 - 71.09 4.0 0.84 - 64.16 4.8 0.14 - 9.1 33.3 0.1 

Toluene 

Composite 7.2 0.25 - 18.47 0.7 0 - 4.75 32.6 7.63 - 71.22 38.0 0.81 - 87 4.5 0.44 - 9.83 16.5 0.6 

1,1,2-Tri- None 44.0 0.45 - 71.65 1.9 0 - 3.06 18.9 2.63 - 96.1 4.4 1.3 - 71.65 6.1 0.07 - 7.57 24.1 0.1 chloroethane Composite 5.8 0.1 - 10.95 1.2 0 - 14.23 11.4 6.07 - 24.91 7.2 0.32 - 52.8 7.2 4 - 15.42 67.1 21.3 Trichloro- None 55.2 0.45 - 71.65 0.1 0 - 3.06 12.4 2.63 - 96.1 31.9 1.3 - 71.65 0.2 0.07 - 7.57 0.1 0.1 ethylene Composite 1.2 0.09 - 19.72 0.1 0 - 4.44 87.7 3.52 - 96.23 4.1 1.31 - 95.23 2.9 0.12 - 8.32 3.3 0.1 Tetrachloro- None 21.8 0.5 - 71.91 0.01 0 - 2.12 62.7 2.95 - 96.01 15.0 1.24 - 69.78 0.1 0.01 - 1.03 0.3 0.1 ethylene Composite 4.9 0.13 - 24.3 0.2 0 - 2.49 38.2 4.11 - 96.09 55.8 1.34 - 94.16 0.1 0.02 - 1.12 0.3 0.1 Xylene (mixed None 12.5 0.64 - 73.25 0.1 0 - 4.72 15.0 3.68 - 28.29 3.0 0.33 - 57.38 2.3 0.16 - 3.27 66.9 0.1 isomers) Composite 1.4 0.17 - 19.17 0.03 0 - 1.9 13.0 6.38 - 28.27 2.6 0.44 - 56.01 1.8 0.79 - 3.51 81.1 20.7Notes: The percentages are the proportion of total mass added to the landfill for each simulation. 90th refers to the 90th percentile risk simulation Diffusion to atmosphere includes diffusion with and without the cover. Degradation includes biodegradation and hydrolysis (hydrolysis was insignificant relative to biodegradation). 
Review Draft Section 3.0 Analysis 

Table 3-6. Percent Mass Loss from the Landfill to the Atmosphere and Groundwater 

Total Loss to Atmosphere Total Loss to Groundwater 

Liner 90th Range 90th Range 

None 27.19 0.01 - 97.92 26.46 0 - 92.11 

All Solvents Composite 31.52 0.01 - 99.17 2.56 0 - 26.44 

Both 29.36 0.01 - 99.17 14.51 0 - 92.11 

Solvent-Specific Mass Losses 

None 14.25 2.86 - 45.53 10.30 0.18 - 47.52 Acetone 

Composite 8.08 2.81 - 51.01 0.25 0 - 2.73 

None 82.56 25.56 - 96.14 15.53 0.68 - 72.01 

Benzene 

Composite 92.27 60.22 - 98.91 1.39 0.08 - 16.24 

None 0.86 0.13 - 4 7.61 0.01 - 22.08 Butanol 

Composite 0.19 0.13 - 4.21 0.01 0 - 1.14 

None 52.25 17.9 - 86.82 36.31 1.47 - 73.74 Chlorobenzene 

Composite 54.98 27.7 - 95.09 7.23 0.4 - 26.44 

None 0.15 0.06 - 2.11 28.89 0.02 - 91.9 Cresols (total) 

Composite 0.33 0.06 - 2.08 0.60 0.02 - 12.12 

None 3.14 0.79 - 14.55 82.67 0.18 - 85.77 Cyclohexanone 

Composite 2.33 0.73 - 10.66 1.06 0.02 - 14.88 

None 31.82 9.55 - 69.59 27.63 1.24 - 72.7 Dichlorobenzene, 1,2- 

Composite 23.28 10.02 - 76.29 1.52 0.16 - 16.87 

None 0.09 0.01 - 0.86 0.06 0 - 36.68 2-Ethoxyethanol 

Composite 0.12 0.01 - 0.96 0.03 0 - 2.01 

None 3.55 0.88 - 16.66 12.50 0.09 - 27.73 Ethyl acetate 

Composite 3.51 0.86 - 18.23 0.09 0 - 0.58 

None 41.90 15.06 - 85.18 33.87 1.48 - 74.14 Ethyl benzene 

Composite 44.32 20.23 - 92.81 14.36 0.34 - 23.76 

None 4.36 0.49 - 17.66 0.90 0.01 - 13.22 Isobutanol 

Composite 1.44 0.48 - 19.45 0.08 0 - 0.61 

None 1.27 0.69 - 19.89 82.77 0.3 - 92.11 Methanol 

Composite 4.13 0.69 - 13 3.51 0 - 22.4 

None 68.23 21.83 - 92.68 20.45 0.4 - 63.85 Methylene chloride 

Composite 81.50 32.95 - 98.16 0.05 0.01 - 1.53 

None 21.88 3.27 - 50.47 8.98 0.16 - 39.39 Methyl ethyl ketone 

Composite 11.70 3.2 - 55.99 0.04 0 - 2.14 

None 10.91 1.77 - 35.8 6.91 0.08 - 25.82 Methyl isobutyl ketone 

Composite 11.71 1.73 - 39.56 0.01 0 - 0.6 

None 43.38 18.5 - 88.76 18.15 1.44 - 74.16 Toluene 

Composite 70.58 27.97 - 96.5 7.41 0.25 - 18.47 

(continued)

Do not cite or quote. 3-25 
Review Draft Section 3.0 Analysis 

Table 3-6. (continued) 

Total Loss to Atmosphere Total Loss to Groundwater 

Liner 90th Range 90th Range 

Solvent-Specific Mass Losses (continued) 

1,1,2-Trichloroethane None 23.28 7.6 - 62.83 45.88 1.36 - 75.38 

Composite 18.59 7.56 - 63.42 6.00 0.1 - 10.95 

Trichloroethylene None 44.28 26.91 - 97.92 55.34 0.5 - 71.65 

Composite 91.84 68.29 - 99.17 1.26 0.09 - 19.9 

Tetrachloroethylene None 77.63 27.05 - 97.81 21.82 0.54 - 71.91 

Composite 94.05 66.52 - 99.02 4.91 0.13 - 24.38 

Xylene (mixed isomers) None 17.99 8.01 - 69.12 12.63 0.8 - 73.25 

Composite 15.55 8.51 - 69.86 1.42 0.17 - 19.17 

Notes: 

The percentages are calculated as the proportion of total mass added to the landfill for each simulation. 

90th refers to the 90th percentile risk simulation. 

Table 3-7. Percent Mass Loss from the Landfill Due to No-Cover Diffusion and Liner 

Leakage 

Mass Loss 

with No Cover 

Liner 

Leakage 

Liner 90th Range 90th Range 

None 2.89 0.01 - 71.33 11.17 

All Solvents Composite 2.26 0.01 - 75.19 0.28 0 - 22.38 

Both 2.57 0.01 - 75.19 5.72 0 - 22.38 

Solvent-Specific Mass Losses 

None 3.68 0.31 - 35.87 8.64 Acetone 

Composite 1.85 0.31 - 39.26 0.23 0 - 2.69 

None 6.70 0.55 - 52.39 0.16 

Benzene 

Composite 15.14 0.55 - 56.44 0.02 0 - 0.87 

None 0.31 0.02 - 3.23 6.86 Butanol 

Composite 0.09 0.02 - 3.6 0.00 0 - 0.99 

None 2.54 0.28 - 32.53 1.54 Chlorobenzene 

Composite 1.95 0.28 - 35.69 0.02 0 - 0.89 

None 0.07 0.01 - 1.6 27.85 Cresols (total) 

Composite 0.16 0.01 - 1.79 0.22 0 - 10.52 

None 0.36 0.05 - 6.52 80.03 Cyclohexanone 

Composite 0.73 0.05 - 7.28 0.74 0 - 13.1 

None 0.99 0.15 - 19.68 0.36 Dichlorobenzene, 1,2- 

Composite 1.48 0.15 - 21.8 0.05 0 - 0.96 

None 0.08 0.01 - 0.82 0.05 2-Ethoxyethanol 

Composite 0.10 0.01 - 0.92 0.02 0 - 1.95 

(continued)

Do not cite or quote. 3-26 
Review Draft Section 3.0 Analysis 

Table 3-7. (continued) 

Mass Loss 

with No Cover 

Liner 

Leakage 

Liner 90th Range 90th Range 

Solvent-Specific Mass Losses (continued) 

None 0.83 0.09 - 11.47 3.07 Ethyl acetate 

Composite 1.32 0.09 - 12.77 0.07 0 - 0.5 

None 3.71 0.22 - 26.62 1.11 Ethyl benzene 

Composite 5.01 0.22 - 29.33 0.13 0 - 0.4 

None 2.03 0.12 - 16.25 0.80 Isobutanol 

Composite 0.49 0.12 - 18.03 0.06 0 - 0.53 

None 0.39 0.06 - 7.71 77.57 Methanol 

Composite 0.88 0.06 - 8.6 3.49 0 - 22.38 

None 11.71 0.97 - 71.33 1.46 Methylene chloride 

Composite 4.59 0.95 - 75.19 0.00 0 - 0.76 

None 2.28 0.37 - 41.06 8.10 Methyl ethyl ketone 

Composite 1.49 0.36 - 44.74 0.03 0 - 1.93 

None 4.24 0.24 - 28.33 3.52 Methyl isobutyl ketone 

Composite 0.62 0.23 - 31.18 0.00 0 - 0.49 

None 1.69 0.29 - 32.61 0.15 Toluene 

Composite 2.14 0.28 - 35.77 0.18 0 - 0.65 

None 1.38 0.14 - 17.25 1.90 1,1,2-Trichloroethane 

Composite 0.94 0.14 - 19.13 0.22 0 - 2.07 

None 6.30 0.58 - 54.29 0.11 Trichloroethylene 

Composite 1.51 0.57 - 58.37 0.01 0 - 0.43 

None 6.62 0.52 - 50.99 0.01 Tetrachloroethylene 

Composite 3.55 0.51 - 55.02 0.00 0 - 0.23 

None 1.89 0.14 - 17.34 0.10 Xylene (mixed isomers) 

Composite 1.09 0.13 - 19.23 0.01 0 - 0.25 

Notes: 

The percentages are calculated as the proportion of total mass added to the landfill for each simulation. 

90th refers to the 90th percentile risk simulation. Mass loss with no cover is a component of diffusion to atmosphere in Table 3-5. 

Liner leakage is a component of aqueous advection in Table 3-5. 

 As seen in Tables 3-5 and 3-7, the mass loss due to liner diffusion exceeds liner leakage 

for all but four solvents (methanol, acetone, 2-ethoxyethanol, and methyl ethyl ketone). 

Generally, constituents that more readily sorb to the geomembrane surface exhibit greater 

diffusive flux through the geomembrane due to the resulting increased concentration gradient 

across the geomembrane. This pattern is reflected in the correlation used to estimate the 

geomembrane permeation coefficient (see Appendix B, equation B-8), whereby the permeation 

coefficient increases with the octanol-water distribution coefficient (Sangham and Rowe, 2001). 

Diffusive transport through the liner exceeds leakage by up to three orders of magnitude. Several 

researchers have documented the importance of diffusion through geomembranes and 

emphasized that this transport mechanism can be much more significant than liner leakage for 

many volatile organic compounds (VOCs) (Park et al. 1996; Edil, 2003; Foose 2002). Park et al. 

(1996) demonstrated that the diffusive mass flux through a geomembrane can be more than two 

Do not cite or quote. 3-27 
Review Draft Section 3.0 Analysis 

orders of magnitude greater than the mass flux due to the typical number and size of holes found 

in landfill geomembranes. The analysis in Foose (2002) demonstrated that diffusive fluxes for 

many VOCs can be four to six orders of magnitude greater than the leakage flux. Therefore, the 

analysis result that diffusion through the geomembrane can exceed leakage by up to three orders 

of magnitude is consistent with the literature. 

3.2 Air Dispersion Modeling 

This section describes the methodology and the model that was used to predict the fate 

and transport of chemical solvents in ambient air after their release from a landfill. Section 3.2.1

provides an overview of the conceptual model, including the basic approach and assumptions. 

Section 3.2.2 discusses the input parameters and values used in this risk analysis. Section 3.2.3

discusses the model outputs. Appendices G and H, listed below, provide additional information 

about the dispersion modeling output and how they were applied in this risk analysis: 

 Appendix G, Unitized Air Concentrations (UACs), describes the linear interpolation 

methodology used in determining the UAC values. 

 Appendix H, Equations, presents the equations that were used to estimate ambient air 

exposure concentrations using the UAC values. 

3.2.1 Conceptual Model 

Air dispersion modeling uses a computer-based set of calculations to estimate ambient 

ground-level constituent concentrations associated with solvent releases from a waste 

management unit (WMU). The dispersion model uses information on meteorology (e.g., wind 

speed, wind direction, temperature) to estimate the movement of solvents through the 

atmosphere. Movement downwind is largely determined by wind speed and wind direction. 

Dispersion around the centerline of the contaminant plume is estimated using empirically derived 

dispersion coefficients that account for horizontal and vertical movement of solvents. 

3.2.1.1 Overview 

In this analysis, modeling was performed to estimate ambient air concentrations for 

volatile solvents released from landfills. Modeling was performed using EPA’s ISCST3 to 

estimate total air concentrations at various locations surrounding the landfills. The air 

concentrations developed by ISCST3 were based on a unit emission rate of 1 μg/s-m2

 and are 

called unitized air concentrations (UACs) (μg/m3

 per unit emission rate of 1 μg/s-m2). As shown 

in Appendix H, the exposure model multiplies the UACs by the constituent-specific emission 

rates (predicted by the landfill source model) and appropriate conversion factors to develop the 

final constituent-specific ambient air estimates (mg/m3

). 

This analysis was conducted using a conceptual site model that could exist anywhere in 

the contiguous 48 states. To capture the national variation in environmental settings, a regionally 

based approach was applied. As a result, modeling was conducted using surface and upper air 

data from 31 different meteorological regions. To capture the range of the municipal landfill size 

distribution, 19 representative surface areas were modeled. Outputs were generated for receptor 

points defined by a polar grid that simulated locating individuals in any of 36 directions at any of 

8 distances. Receptors were placed every 10° around the full 360° surrounding the landfill on 

each distance ring. The ring distances modeled in this analysis ranged from 25 to 2,000 m from 

the edge of the unit. 

Do not cite or quote. 3-28 
Review Draft Section 3.0 Analysis 

The resulting UACs were indexed based on site location, landfill surface area, receptor 

distance, and receptor direction. These data were imported into a database that was used to 

construct a subset of 10,000 scenario records for use in the Monte Carlo analysis. To construct 

this data set, it was necessary to define the site location, landfill surface area, receptor distance, 

and receptor direction for each scenario record. The approach used to define these characteristics 

was as follows: 

 Site location. The EPACMTP Climate Center locations were used to place landfill 

locations, as described in Section 2.2.4. For dispersion modeling, these locations were 

cross-referenced to one of the 57 IWAIR meteorological regions. As a result, 31 different 

meteorological regions were identified and assigned to each of the 10,000 records. 

 Surface Area. The record-specific surface areas were set to the same landfill surface 

areas used as input to the LFCR model. 

 Receptor Distance. As described in Section 3.2.2, the receptor distances were randomly 

selected. 

 Receptor Direction. Each of the 10,000 records was associated with a single direction 

that was randomly selected from one of the 36 directions. 

Based on these record-specific indices, an interpolation application selected an 

appropriate value from the UAC data set. In the event that the scenario-specific surface area fell 

between two of the representative sizes, an interpolation method estimated a UAC in relation to 

the modeled landfill sizes. The interpolation method is consistent with the methodology 

employed in the IWAIR model (U.S. EPA, 2002a). The UAC methodology is further discussed 

in Appendix G. 

3.2.1.2 Key Assumptions 

This section presents key assumptions associated with the air modeling approach. More 

comprehensive documentation of the ISCST3 model and associated assumptions are available in 

the User’s Guide for the Industrial Source Complex (ISC3) Dispersion Model (U.S. EPA, 

1995a). 

 The distance and direction between the landfill source and the receptor was assumed to 

be random. 

 The model does not include photochemical reactions or degradation of constituents in the 

air, which results in additional model uncertainty for some solvents. 

 ISCST3 is assumed to provide an accurate approximation of the transport of volatile 

solvents from landfill sources in the atmosphere. 

 The model assumes average meteorological conditions (5-year averages). Thus, it is 

assumed that transient conditions are insignificant in approximating long-term average 

risk for this analysis. 

3.2.2 Model Inputs 

For the Monte Carlo simulations, each of the 10,000 scenario records was associated with 

UACs that were indexed based on site location, landfill surface area, receptor distance, and 

receptor direction. Each of these indices and other inputs to ISCST3 are discussed below. 

Do not cite or quote. 3-29 
Review Draft Section 3.0 Analysis 

Site Location. EPACMTP Climate Center locations were used to place landfill locations, 

as described in Section 2.2.4. Each location was then cross-referenced to one of the 57 

meteorological regions developed for the IWAIR model. As a result, 31 different meteorological 

regions were identified. For each landfill location, modeling was conducted using 5 years of 

existing surface and upper air meteorological data that were preprocessed and used in the 

development of IWAIR. 

Landfill Surface Area. As discussed in 

Section 3.1.2.4, landfill surface areas modeled in the 

analysis were based on the EPA-WBJ data. Because 

each of the 10,000 simulations was associated with a 

unique surface area, it was not feasible to perform 

dispersion modeling for all surface areas considered in 

the analysis. Instead, dispersion modeling was 

performed for representative surface areas and a linear 

interpolation was performed, as discussed further 

below. For this analysis, ISCST3 modeling was 

conducted for landfills with surface areas that are 

representative of the municipal landfill distribution 

discussed in Section 3.1.2.4 (i.e., ranging from 4,000 

m

2

 to approximately 17,000,000 m2). To represent this 

range, 19 surface areas were identified for modeling in the current analysis (see text box). 

Landfill Areas Modeled 

4,000 m2 420,000 m2

6,045 m2 607,000 m2

8,090 m2 906,528 m2

14,145 m2 1,157,442 m2

20,200 m2 1,408,356 m2

40,450 m2 3,146,678 m2

60,700 m2 4,885,000 m2

127,350 m2 9,350,000 m2

194,000 m2 17,000,000 m2

307 000 m2

To ensure that the areas described above were appropriate for linear interpolation, an 

analysis was performed that was similar to one implemented as part of the development of 

IWAIR. The goal of this analysis was to evaluate the adequacy of the selected areas in achieving 

a goal of less than 5 percent interpolation error. Under the current effort, interpolation errors 

were computed using ISCST3 outputs generated for the surface areas described above, as well as 

for areas representing the midpoints between each size. Modeling was performed for the 

representative surface areas using meteorological data for three locations included in IWAIR: 

Fresno, CA; Minneapolis, MN; and Salt Lake City, UT. These stations represent a range of wind 

roses and climate regimes.15 Results from the analysis indicated that the goal of less than 5 

percent interpolation error was achieved. As a consequence, the 19 surface areas described above 

were used for the current analysis. 

Receptor Distance. Eight ring distances were used in this analysis (see text box). These 

ring distances represent the range of distances included in prior analyses and models, such as the 

3MRA modeling system. The specific distances were selected to 

ensure complete coverage in the air estimates, particularly near the 

source of the emissions. As input to the Monte Carlo analysis, the 

10,000 scenario distances were randomly selected from these ring 

distances. 

Ring Distances 

25 m 300 m 

50 m 500 m 

75 m 1,000 m Receptor Direction. ISCST3 modeling was performed using 

a polar grid that simulated locating receptors in any of 36 directions 

(i.e., on each distance ring, receptors were placed every 10° around 

150 m 2,000 m 

 

15 For a given location, a wind rose shows the frequency of wind directions about a 16-point compass, and uses 

different shading to represent wind speeds (U.S. EPA, 2006b). 

Do not cite or quote. 3-30 
Review Draft Section 3.0 Analysis 

the full 360° surrounding the landfill). In the Monte Carlo analysis, each of the 10,000 records 

was associated with a single direction that was randomly selected from one of these 36 

directions. 

Source Shape. Landfills were modeled as ground-level sources using a 20-sided polygon 

shaped to approximate a circle. This option was chosen because there are no actual data on the 

shape of sources, and a circular source will minimize air modeling errors caused by source 

shapes and orientation. 

Rural vs. Urban. Similar to the IWAIR modeling, the rural setting was used to reflect 

typical landfill locations (i.e., landfills are typically not located in large cities). 

3.2.3 Model Outputs 

The ISCST3 outputs were processed and imported into a database that served as the basis 

for determining the scenario-specific UACs [(μg/m3) per (unit emission rate of 1 g/s-m2)] that 

were used as input for the exposure and risk model. The resulting database includes UACs that 

represent all modeled locations, 19 representative surface areas, 8 distances, and 36 directions. 

These data were used by an interpolation application to determine a UAC for each of the 10,000 

scenarios. The resulting data set was then imported into the exposure and risk model database 

and used with volatile emission rates (g/s-m2

) calculated by the source models to determine 

ambient air concentrations. For each run ID, air concentrations of volatile solvents were 

calculated for each year of emissions. Based on this time series of concentrations, the maximum, 

or peak, concentration was identified. The timing of the maximum ambient air concentration 

represented the midpoint of exposure. Thus, the time period of exposure was centered on the 

peak (or maximum) ambient air concentration. The average air concentration across this time 

period was used to estimate inhalation exposures. Appendix H presents the equations that were 

used to estimate ambient air exposures. 

3.3 Groundwater Modeling 

This section describes the methodology and the models that were used to predict the fate 

and transport of solvents from a landfill to underlying soils and groundwater to determine 

impacts on drinking water wells that are connected to groundwater. Section 3.3.1 provides an 

overview of the conceptual model, including the basic approach and assumptions. Section 3.3.2

discusses the input parameters and values used in this risk analysis. Section 3.3.3 discusses the 

model outputs. Appendices F and H provide additional information about the groundwater 

modeling and how it was used in the exposure model: 

 Appendix F, Groundwater Modeling Parameters, presents the input values and the 

distributions used in modeling the groundwater pathway using EPACMTP. 

 Appendix H, Equations, presents the equations that were used to estimate groundwater 

exposures. 

3.3.1 Conceptual Model 

The groundwater pathway was modeled for this analysis to estimate the receptor 

concentrations that result from a predicted release of solvents from landfills. The release of a 

solvent occurs when liquid that percolates through the landfill generates leachate, which can 

infiltrate from the bottom of the landfill into the subsurface. 

Do not cite or quote. 3-31 
Review Draft Section 3.0 Analysis 

The waste solvents dissolved in the leachate are transported via aqueous-phase migration through 

the unsaturated zone (the soil layer under the landfill) to the underlying saturated zone (i.e., 

groundwater), and then downgradient to a hypothetical receptor. For this analysis, the exposure 

concentration was evaluated as the intake point of a hypothetical residential drinking water well 

(hereafter referred to as the “receptor well”). Figure 3-5 shows the conceptual model of the 

groundwater fate and the transport of contaminant releases from a landfill to a downgradient 

receptor well with associated dilution and attenuation. Details about the modeled receptor well 

are provided in Section 3.3.1.1. Appendix H presents the equations that were used to estimate 

ingestion exposures resulting from the consumption of drinking water. 

Figure 3-5. Schematic diagram of groundwater modeling scenario. 

3.3.1.1 Overview 

The transport of leachate from the landfill through the unsaturated and saturated zones is 

evaluated quantitatively using EPACMTP (U.S. EPA, 2003a,c,d; 1997b). EPACMTP simulates 

the flow and transport of contaminants in the unsaturated zone and aquifer beneath a waste 

disposal unit to yield the concentration that arrives at a specified receptor location. The LFCR 

source model described in Section 3.1 determines the leachate concentration used as input to 

EPACMTP. New functionality was added to the EPACMTP model, as described in the 

addendum to the EPACMTP Technical Background Document (U.S. EPA, 2003d), to create a 

dynamic, mass-conserving linkage between the source models and EPACMTP. 

The groundwater model accounts for advection, hydrodynamic dispersion, equilibrium 

linear or nonlinear sorption, and transformation processes via chemical hydrolysis. In this 

analysis, data were compiled from the scientific literature to develop organic carbon partition 

coefficients to simulate equilibrium linear partitioning for organic constituents. 

EPACMTP is a composite model that consists of two coupled modules: (1) a 1-D module 

that simulates infiltration and dissolved contaminant transport through the unsaturated zone, and 

(2) a saturated zone flow and transport module that includes three groundwater transport solution 

options: (i) fully 3-D transport, (ii) quasi-3-D transport (a combination of cross-sectional and 

areal solutions), and (iii) pseudo-3-D transport (hybrid analytical and numerical solution). The 

applicability and appropriateness of each of the transport solution options depend on the problem 

Do not cite or quote. 3-32 
Review Draft Section 3.0 Analysis 

considered. The pseudo-3-D solution is the most computationally efficient of the available 

options, and allows more Monte Carlo realizations to be included in the modeling analysis. In 

addition, the pseudo-3-D solution can accurately and efficiently generate full breakthrough 

curves at the receptor location. For these reasons, the pseudo-3-D solution option was chosen for 

this analysis. 

The EPACMTP model algorithms are based on the assumption that the soil and aquifer 

are uniform porous media, and that flow and transport are described by Darcy’s law and the 

advection-dispersion equation, respectively. EPACMTP does not account for preferential 

pathways, such as fractures and macropores, or facilitated transport, which may affect the 

migration of strongly sorbing constituents. 

The primary transport mechanisms in the subsurface are the downward migration of 

dissolved solvents within infiltrating water in the unsaturated zone, and lateral movement of 

solvents along with the ambient groundwater flow in the saturated zone. The advective 

movement in the unsaturated zone is 1-D, whereas the saturated zone module accounts for 3-D 

flow and transport. The model also considers mixing due to hydrodynamic dispersion in both the 

unsaturated and saturated zones. In the unsaturated zone, flow is gravity driven and prevails in 

the vertically downward direction. Therefore, the flow is modeled in the unsaturated zone as 1-D 

in the vertical direction. It is also assumed that transverse dispersion (both mechanical dispersion 

and molecular diffusion) is negligible in the unsaturated zone. This assumption is based on the 

fact that lateral migration due to transverse dispersion is negligible compared with the horizontal 

dimensions of the WMUs. In addition, this assumption is environmentally protective because it 

allows the leading front of the constituent plume to arrive at the water table with greater peak 

concentration in the case of a finite source. 

In the saturated zone, the movement of constituents is primarily driven by ambient 

groundwater flow, which in turn is controlled by a regional hydraulic gradient and hydraulic 

conductivity in the aquifer formation. The model takes into account the effects of infiltration 

from the waste source, as well as regional recharge into the aquifer. The effect of infiltration 

from the waste source is an increase in groundwater flow in the horizontal transverse and vertical 

directions underneath and in the immediate vicinity of the waste source, as may result from 

groundwater mounding. This 3-D flow pattern will enhance the horizontal and vertical spreading 

of the plume. Regional recharge outside of the waste source causes a (vertically) downward 

movement of the plume as it travels in the (longitudinally) downgradient groundwater flow 

direction. In addition to advective movement and groundwater flow, the model simulates the 

mixing of contaminants with groundwater due to hydrodynamic dispersion, which acts in the 

longitudinal direction (i.e., along the groundwater flow direction), as well as in horizontal and 

vertical transverse directions. 

Leachate constituents can be subject to complex geochemical interactions in soil and 

groundwater, which can strongly affect their rate of transport in the subsurface. EPACMTP treats 

these interactions as equilibrium sorption processes. The equilibrium assumption means that the 

sorption process occurs instantaneously or at least very quickly relative to the time scale of 

constituent transport. Although sorption, or the attachment of leachate constituents to solid soil 

or aquifer particles, may result from multiple chemical processes, EPACMTP combines these 

processes into an effective soil-water partition coefficient (Kd). The retardation factor, R, 

accounts for the effects of equilibrium sorption of dissolved constituents onto the solid phase. R, 

a function of the constituent-specific Kd and the soil or aquifer properties, is calculated as 

follows: 

Do not cite or quote. 3-33 
Review Draft Section 3.0 Analysis 

φ

ρ Kdb R 1+= (Equation 3-1) 

where 

R = retardation factor (unitless) 

ρb = soil or aquifer bulk density (g/cm3

) 

Kd = solid-water partition coefficient (cm3

/g) 

φ = water content (in unsaturated zone) or porosity (in saturated zone) (unitless). 

As modeled in EPACMTP, the Kd of an organic constituent is assumed to be constant and 

is calculated as the product of the mass fraction of organic carbon in the soil or aquifer and a 

constituent-specific organic carbon partition coefficient (Koc). 

As can be seen in Equation 3-1, constituents with a low Kd (or Koc) value will have an R

close to 1.0, which indicates that they will move at nearly the same velocity as the groundwater. 

Constituents with high Kd values, such as certain semivolatile organic constituents and many 

metals, will have high R values and may move many times more slowly than the groundwater. 

The concentration of constituents in both the unsaturated zone and the saturated zone 

may be further attenuated by biochemical transformation reactions. EPACMTP can, in principle, 

model a wide range of transformation processes, as long as they can be combined as a first-order 

biodegradation process or a first-order chemical degradation process. In practice, a more 

conservative approach was adopted in which only the degradation of organic constituents via 

hydrolysis transformation processes was simulated. For those constituents that may be 

transformed via hydrolysis into toxic daughter products, EPACMTP can account for the 

formation and subsequent fate and transport of the daughter products. 

Hydrolysis rates were calculated from chemical-specific hydrolysis rate constants using 

soil and aquifer temperature and pH values. Whereas chemical hydrolysis can be accurately 

modeled as a function of these factors, many other types of transformation processes, such as 

biodegradation, are much more site specific and can be highly variable—and therefore, much 

more difficult to simulate using a generic model such as EPACMTP. Thus, for this analysis, it 

was conservatively assumed that these processes do not occur; that is, biodegradation was not 

included in the subsurface environment beyond the landfill. 

The EPACMTP model simulates steady-state flow in both the unsaturated and saturated 

zones, and can accommodate either steady-state or transient contaminant transport. Steady-state 

transport modeling is a very protective modeling approach in which a WMU continues to release 

contaminants indefinitely (continuous source); eventually, the model will predict that the 

receptor well concentration reaches a constant value. However, in this analysis, transient 

transport simulations were performed. As discussed in Section 2.2.3, this finite source approach 

simulates the amount of time over which the landfill operates and the time-dependent movement 

of chemical constituents in the subsurface to the receptor well. 

Do not cite or quote. 3-34 
Review Draft Section 3.0 Analysis 

Groundwater Receptor 

One of the most important inputs for EPACMTP is receptor location, which for this risk 

analysis included residential drinking water wells. EPACMTP can evaluate the exposure 

concentration at the intake point of a hypothetical residential drinking water well located at a 

specified distance from the downgradient edge of the WMU and at a specified depth below the 

water table. The location of the receptor well intake point is ultimately defined in terms of its x, 

y, and z coordinates relative to the center of the WMU’s downgradient edge (see Figure 3-6 [b], 

in which xrw refers to the downgradient distance from the edge of the WMU, yrw refers to the 

horizontal transverse distance from the plume centerline, and zrw (not shown in the figure) refers 

to the vertical distance below the water table). The planar coordinates (xrw and yrw) may be 

explicitly specified or they may be derived from a radial distance (Rrw) and angle of rotation (θrw) 

off the plume centerline, as shown in Figure 3-6 (a). The shaded areas in Figure 3-6 (a and b) 

conceptually represent the horizontal extent of the contaminant plume. 

Figure 3-6. Schematic plan view showing procedure for determining the downstream 

location of the receptor well: (a) well location determined using radial distance, 

Rrw, and angle off center, θrw; and (b) well location generated randomly within 

plume limit. 

Do not cite or quote. 3-35 
Review Draft Section 3.0 Analysis 

In this analysis, the xrw and yrw distances are derived from radial distance Rrw and angle 

θrw and are defined as follows: 

xrw = Rrw cos(θrw) 

yrw = Rrw sin(θrw) or the distance from the plume centerline to the plume edge, whichever 

is smaller. 

The zrw distance is defined as follows: 

zrw = 0 to 10 m (or to the base of the aquifer if the aquifer thickness is less than 10 m) 

measured downward from the water table. 

The well was placed at a downgradient distance of up to 1 mile, based on a nationwide 

distribution of the nearest downgradient residential wells from a survey of Subtitle D MSWLFs 

(U.S. EPA, 1993a). This distribution was used to represent Rrw and is presented in Appendix F. 

It was assumed that the same distribution holds for other types of landfills as well. A uniform 

distribution within the range of 0° to 90° was used to model θrw. The limits on yrw locate the 

receptor well within the lateral extent of the plume (because of symmetry around the plume 

centerline, only half of the lateral extent of the plume is actually modeled). 

The distance from the plume centerline to the lateral edge of the plume, which is used to 

constrain yrw, is approximated as follows: 

( ) T rw Plume Edge = 5.0 AREA α ∗+ 23 + xAREA (Equation 3-2) 

where 

AREA = area of an assumed square WMU (m2) 

αT = horizontal transverse aquifer dispersivity (m) 

 xrw = downgradient distance of receptor well from the edge of the WMU (m). 

The first term on the right side of Equation 3-2 represents the contribution from the 

WMU area itself to the extent of the plume. The factor 0.5 represents the fact that the plume edge 

is calculated from the plume centerline (i.e., only half of the actual plume width is considered). 

The second term on the right side represents the lateral spreading of the plume due to transverse 

dispersion, with increasing downgradient distance from the WMU. The magnitude of dispersive 

effects is expressed in the αT parameter. The AREA and xrw terms represent the total 

downgradient plume travel distance, starting from the upgradient edge of the landfill. 

The approximation used above for the lateral extent of the contaminant plume is based on 

the assumption that the spreading of the plume in the horizontal transverse direction is caused by 

dispersive mixing, which results in a Gaussian profile of the plume cross-section. Under this 

assumption, this approximation of the location of the plume boundary will capture 99.7 percent 

of the contaminant mass. However, this approximation does not take into account advective 

spreading of the plume due to lateral groundwater flow in the vicinity of the WMU and may 

therefore underestimate the actual lateral extent of the plume. However, in this case, the 

approximation was conservative, as it prevented placement of the well at the outer fringes of the 

plume, which would have a lower concentration compared with that of a well closer to the plume 

centerline. 

Do not cite or quote. 3-36 
Review Draft Section 3.0 Analysis 

When EPACMTP was originally developed, the angle of the receptor well off of the 

plume centerline was typically generated randomly, and xrw and yrw were derived from Rrw and 

θrw. However, since that time, an improvement has been made to the modeling methodology that 

seeks to reduce the potential bias in observed concentrations associated with large WMU areas 

when the observation location is derived relative to the center of the downgradient edge of the 

WMU. This improved methodology, detailed in the EPACMTP Technical Background 

Document (U.S. EPA, 2003c), was used in the current analysis and allows the point of origin for 

the receptor location to move to the corner of the idealized WMU when the size of the WMU is 

large relative to Rrw. Regardless of the point of origin, the constraint on yrw relative to the edge of 

the plume still applies. 

For this analysis, the depth of the well was varied uniformly throughout the aquifer 

thickness or throughout the upper 10 m of the aquifer thickness, whichever was less. That is, the 

well depth was never allowed to exceed 10 m below the water table. This limitation for well 

depth has been used in previous analyses primarily for two reasons: (1) to be consistent with a 

residential well scenario (these wells are generally shallow because of the higher cost of drilling 

a deeper well) and (2) to produce a conservative estimate of risk (because the infiltration rate is 

generally lower than the groundwater seepage velocity, groundwater plumes tend to be relatively 

shallow). 

Groundwater Pathway Simulations

The leachate fluxes (g/m2/yr) estimated by the landfill source model were used as input to 

the groundwater fate and transport model, EPACMTP, to generate constituent concentrations at 

receptor wells and associated groundwater dilution and attenuation factors (DAFs). A peak DAF 

is the ratio of the leachate concentration to the maximum 1-year observed receptor well 

concentration. Likewise, an N-Year Average DAF is the ratio of the leachate concentration to the 

maximum N-year observed receptor well concentration (both definitions are based on an 

assumed constant initial leachate concentration). In other words, the DAF represents the 

reduction in constituent concentration that occurs before the leachate reaches the well. The DAF 

is a dimensionless quantity, the value of which can vary from one to infinity. A DAF value of 1 

corresponds to a relative receptor well concentration of 1. This situation means that the exposure 

concentration at the receptor well is the same as the leachate concentration that enters the 

subsurface from the modeled WMU, and no dilution or attenuation occurs along the subsurface 

pathway. Conversely, if the contaminant plume does not reach the receptor well at all, the 

receptor well concentration will be zero and the corresponding DAF will approach infinity. 

For the organic solvents considered in this analysis, the fate and transport equations in 

groundwater model are linear (i.e., the model produces a linear isotherm); therefore, the 

predicted groundwater well concentration is linearly proportional to the value of the leachate 

concentration. That is, if we doubled the solvent concentration in leachate as input to 

EPACMTP, the model would predict a maximum concentration at the groundwater well that was 

double the original value, provided all other model parameters remain the same. The practical 

implication of this linearity is that once the DAFs have been calculated, they can be used to 

estimate the groundwater well concentration at specific percentiles (e.g., 90th percentile) for any 

leachate concentration. 

Do not cite or quote. 3-37 
Review Draft Section 3.0 Analysis 

For the groundwater pathway simulations in this analysis, the flux of solvent from the 

landfill was not constant. Instead, the source model predicts a time series of leachate flux, 

whereby the mass transfer to the groundwater pathway varies from year to year. In this case, the 

degree of dilution and attenuation was calculated using the maximum calculated leachate flux for 

each run, or an effective leachate flux/concentration. Hence, the DAF was considered an 

effective DAF, or DAFEff, and the same properties of the DAF hold true for effective DAF. 

Therefore, constituents with similar fate and transport properties (including volatility) would be 

expected to have similar effective DAFs. 

Conceptually, each source model leachate profile processed by EPACMTP represents 

one possible outcome, and each realization has an equal probability of occurrence. The part of 

the Monte Carlo simulation performed with EPACMTP (10,000 flow and transport realizations) 

resulted in a distribution of predicted receptor well concentrations and their corresponding 

effective DAFs; these results can be summarized by constructing a CDF and selecting any 

percentile of interest. For example, the 10th percentile effective DAF is the DAF that 

corresponds to the lowest 10 percent of all the cases simulated, and is referred to as the DAFEff

10. 

Tables 3-8 and 3-9 provide a summary, by solvent and liner scenario, of the DAFEff

10 

and the DAFEff

50 corresponding to the peak 1-year receptor well concentration and two maximum 

average concentrations using averaging periods corresponding to exposure periods for a child (7 

years) and adult receptor (30 years). The DAFEff

10 and the DAFEff50 are the DAFs that were used 

to calculate the 90th percentile and the 50th percentile groundwater concentrations, respectively. 

The median groundwater retardation coefficients and the median biochemical hydrolysis 

degradation rates (lambda, yr-1) are also presented by solvent. For a given liner scenario, solvents 

with similar values of retardation and hydrolysis have similar values for the DAFEff

10. 

Do not cite or quote. 3-38 
Review Draft Section 3.0 Analysis 

Table 3-8. Groundwater Pathway DAFEff

10 

Solvent CAS No. Retardationa

Hydrolysisa

[yr-1] Liner 

Peak 

DAFEff10

Child 

DAFEff10

Adult 

DAFEff10

NL 1.95 1.95 2 

Methanol 67-56-1 1 0 

GM 891 904 923 

Ethoxyethanol, 2- NL 2.13 2.16 2.27 

(ethylene glycol 

monoethyl ether) 

110-80-5 1.001 0 

GM 2430 2510 2550 

NL 2.82 2.93 3.15 

Methyl ethyl ketone 78-93-3 1.003 0 

GM 8830 9010 9390 

NL 3.37 3.51 3.79 

Isobutyl alcohol 78-83-1 1.009 0 

GM 8840 8880 8950 

Butanol, n- (n-butyl NL 2.06 2.08 2.18 

alcohol) 71-36-3 1.01 0 

GM 1270 1280 1280 

NL 2.76 2.83 3.02 

Methyl isobutyl ketone 108-10-1 1.023 0 

GM 8230 8430 8640 

NL 2.25 2.27 2.39 

Benzene 71-43-2 1.198 0 

GM 2230 2240 2290 

NL 1.99 2 2.04 

Cyclohexanone 108-94-1 1.207 0 

GM 915 915 915 

NL 2.33 2.36 2.46 

Trichloroethylene 79-01-6 1.395 0 

GM 3100 3170 3230 

Cresol mixtures NL 2 2 2.04 

(Tricresol) 1319-77-3 1.414 0 

GM 1200 1220 1250 

Tetrachloroethylene NL 2.4 2.43 2.53 

(perchloroethylene) 127-18-4 1.509 0 

GM 3180 3180 3220 

NL 2.25 2.26 2.28 

Toluene 108-88-3 1.846 0 

GM 2480 2490 2530 

NL 2.33 2.34 2.37 

Chlorobenzene 108-90-7 2.189 0 

GM 3570 3580 3610 

NL 2.48 2.48 2.49 

Ethylbenzene 100-41-4 4.141 0 

GM 8630 8700 8850 

NL 2.34 2.34 2.36 

Xylenes (total) 1330-20-7 4.766 0 

GM 9480 9550 9680 

Dichlorobenzene, 1,2- NL 2.47 2.48 2.5 

(o-) 95-50-1 4.775 0 

GM 9.66E+03 9.78E+03 9.88E+03 

NL 2.52 2.58 2.75 

Acetone 67-64-1 84.4 0 

GM 7370 7660 7860 

Methylene chloride NL 2.58 2.67 2.83 

(dichloromethane) 75-09-2 1.027 2.30E-04 

GM 7850 8050 8390 

Trichloroethane, 1,1,2- NL 2.14 2.15 2.18 

(vinyl trichloride) 79-00-5 1.169 7.48E-04 

GM 2550 2590 2660 

NL 7.09 7.15 7.57 

Ethyl acetate 141-78-6 1.007 7.94E-02 

GM 5.3E+10 5.3E+10 5.3E+10 

a Based on median aquifer values. 

Do not cite or quote. 3-39 
Review Draft Section 3.0 Analysis 

Table 3-9. Groundwater Pathway DAFEff

50 

 Hydrolysisa Peak Child Adult 

Solvent CAS No. Retardationa [yr-1] Liner DAFEff10 DAFEff10 DAFEff10

NL 32 32.1 33 Methanol 67-56-1 1 0 

GM 1.91E+12 2.49E+12 2.96E+12 

Ethoxyethanol, 2- NL 38.9 39.4 40.2 

(ethylene glycol 

monoethyl ether) 

110-80-5 1.001 0 

GM 1.06E+12 1.23E+12 1.27E+12 

NL 59.1 59.9 64.7 Methyl ethyl ketone 78-93-3 1.003 0 

GM 1.94E+12 2.3E+12 2.52E+12 

NL 69.8 71.7 78.4 Isobutyl alcohol 78-83-1 1.009 0 

GM 1.31E+10 1.36E+10 1.4E+10 

Butanol, n- (n-butyl NL 2.06 2.08 2.18 

alcohol) 71-36-3 1.01 0 

GM 1270 1280 1280 

NL 56.8 57.8 61.3 Methyl isobutyl ketone 108-10-1 1.023 0 

GM 9.36E+11 1.23E+12 1.38E+12 

NL 47.5 48.3 49.7 Benzene 71-43-2 1.198 0 

GM 1.94E+12 2.3E+12 2.48E+12 

NL 33.6 33.9 34.5 Cyclohexanone 108-94-1 1.207 0 

GM 4.21E+09 4.6E+09 4.67E+09 

NL 52.5 53 55.2 Trichloroethylene 79-01-6 1.395 0 

GM 7.97E+12 9.16E+12 1.04E+13 

Cresol mixtures NL 34.4 34.5 34.7 

(Tricresol) 1319-77-3 1.414 0 

GM 8.99E+11 1.03E+12 1.19E+12 

Tetrachloroethylene NL 52.7 53.6 55.4 

(perchloroethylene) 127-18-4 1.509 0 

GM 1.05E+13 1.12E+13 1.23E+13 

NL 47.9 48.3 49.7 Toluene 108-88-3 1.846 0 

GM 2.19E+13 2.48E+13 2.68E+13 

NL 43.2 43.4 44.3 Chlorobenzene 108-90-7 2.189 0 

GM 1.31E+14 1.34E+14 1.42E+14 

NL 71.5 72 72.7 Ethylbenzene 100-41-4 4.141 0 

GM 1.66E+15 1.82E+15 2.19E+15 

NL 65.5 65.7 66.4 Xylenes (total) 1330-20-7 4.766 0 

GM 5.84E+15 7.04E+15 7.1E+15 

Dichlorobenzene, 1,2- NL 68 68.1 68.8 

(o-) 95-50-1 4.775 0 

GM 7.40E+15 9.17E+15 1.06E+16 

NL 51.3 52.2 56.2 Acetone 67-64-1 84.4 0 

GM 9.21E+12 1.24E+13 1.31E+13 

Methylene chloride NL 62.5 63.5 67.5 

(dichloromethane) 75-09-2 1.027 2.30E-04 

GM 3.33E+16 5.85E+16 8.07E+16 

Trichloroethane, 1,1,2- NL 50.9 51.1 52.4 

(vinyl trichloride) 79-00-5 1.169 7.48E-04 

GM 1E+30 1E+30 1E+30 

NL 31700 32300 34200 Ethyl acetate 141-78-6 1.007 7.94E-02 

GM 1E+30 1E+30 1E+30 

a

Base on median aquifer values 

Do not cite or quote. 3-40 
Review Draft Section 3.0 Analysis 

3.3.1.2 Key Assumptions 

This section presents key assumptions associated with the groundwater modeling 

approach. More comprehensive documentation of the EPACMTP model and associated 

assumptions are available in the EPACMTP Technical Background Document (U.S. EPA, 

2003c). 

 The model assumes that the vertical migration is 1-D and that transverse dispersion is 

negligible in the unsaturated zone. 

 The model assumes linear and equilibrium sorption and homogeneous aquifer conditions. 

 The model assumes that receptors use the uppermost aquifer, rather than a deeper aquifer, 

as a source of drinking water. Uppermost aquifers can be used as a drinking water 

source, depending on various characteristics such as the permeability of local soils. This 

assumption could overestimate risks in cases in which the uppermost aquifer is not used. 

 The model assumes that long-term average conditions are sufficient and that shorter 

frequency fluctuations (e.g., in rainfall/infiltration) are insignificant in estimating 

long-term risk. 

 Biodegradation in groundwater was excluded given its significant variability and 

uncertainty. 

 Preferential flow in karst aquifers or in fractures was not considered, although such 

conditions are known to exist over broad areas. Preferential flow can allow contamination 

to migrate faster and at a higher concentration than in a standard porous medium. 

However, the contamination typically does not spread over such a broad area. As a result, 

the modeling may under- or overestimate the concentrations in groundwater. 

 The distribution of receptor well distances and angles from the source was based on an 

approach described in Section 3.3.1.1 and in the EPACMTP Parameters/Data 

Background Document (U.S. EPA, 2003a). It is assumed that the distribution provides a 

reasonable representation of nationally variable conditions. 

3.3.2 Model Inputs 

Appendix F presents the input values and the distributions used in modeling the 

groundwater pathway using EPACMTP. Below is a description of some key EPACMTP inputs. 

One of the most important inputs for EPACMTP is receptor location, which for this risk 

analysis includes residential drinking water wells. As discussed in Section 3.3.1.1, the well was 

placed at a downgradient distance of up to 1 mile, based on a nationwide distribution of the 

nearest downgradient residential wells from a survey of Subtitle D MSWLFs (U.S. EPA, 1993a). 

The leachate fluxes (g/m2

/yr) estimated by the landfill source model were used as input to 

the groundwater fate and transport model to generate constituent concentrations at receptor wells 

and associated groundwater DAFs. All leachate fluxes from the source model were applied 

uniformly over the footprint of the landfill at the top of the unsaturated soil column. Section 

3.3.1.1 provides a detailed discussion of how the leachate fluxes were used in groundwater 

pathway simulations. 

Do not cite or quote. 3-41 
Review Draft Section 3.0 Analysis 

EPACMTP requires information about soil and aquifer properties as model inputs. For 

soils, EPACMTP uses soil texture as a key to generate consistent hydrological properties for the 

unsaturated zone model. As shown in the Vadose section of Table F-1 in Appendix F, sitespecific soil texture (WMU variable ISTYPE1) was used to determine appropriately 

parameterized distribution functions for unsaturated zone model input parameters from which 

random values for each input were selected. These model inputs include saturated hydraulic 

conductivity (SATK), van Genuchten soil moisture parameters (ALPHA and BETA), residual 

and saturated water contents (WCR and WCS, respectively), percent organic matter (POM), and 

oil bulk density. 

Similarly, the hydrogeological setting assigned to each landfill was used to select 

appropriate aquifer conditions from EPACMTP’s HGDB (see Table F-2 in Appendix F). Given 

an aquifer code setting for a landfill (WMU variable IGWR), a correlated sample of key aquifer 

model input parameters (hydraulic conductivity [XKX], hydraulic gradient [GRADNT], depth to 

the water table [DSOIL], and saturated thickness [ZB]) was selected from a population of 

samples taken from similar hydrogeological settings. Details of the data used to parameterize the 

unsaturated zone and the development and use of the HGDB are given in the EPACMTP 

Parameters/Data Background Document (U.S. EPA, 2003a). 

Recharge is water percolating through the soil to the aquifer outside the footprint of the 

WMU. Typically, EPACMTP selects a recharge rate using a meteorological station assignment 

(based on the geographic location and topography of a WMU setting) and by the WMU’s 

associated soil texture mentioned above. Using the soil texture and station assignment, a 

recharge rate was selected from a database of HELP model–derived recharge rates for climate 

stations across the country and for various soil textures. Further details about how these rates 

were determined and other options for determining recharge rates outside of the EPACMTP 

model can be found in the EPACMTP Parameters/Data Background Document (U.S. EPA, 

2003a). 

3.3.3 Model Outputs 

The output of EPACMTP is a prediction of the contaminant concentration arriving at a 

downgradient groundwater receptor location, and is a time-dependent concentration, 

corresponding to the finite source scenario. The model can calculate both the peak concentration 

arriving at the well and maximum time-averaged concentrations. In this analysis, maximum 

time-averaged concentrations were calculated for all solvents based on the exposure duration for 

each receptor type. Because the subsurface migration of some highly sorbing solvents may be 

very slow, it may take a long time for the plume to reach the receptor well, and the maximum 

exposure may not occur until a very long time after the WMU ceases operations. This time delay 

may be on the order of thousands of years. In this analysis, a time period of 10,000 years 

following landfill closure was modeled. If the model predicts that the maximum exposure will 

not have occurred after 10,000 years, the actual receptor concentration at 10,000 years was used 

in the risk calculations. 

Do not cite or quote. 3-42 
Review Draft Section 3.0 Analysis 

3.4 Shower Modeling 

This section describes the methodology and the models that were used to predict the 

concentration of solvents in shower and bathroom air, to which a receptor would be exposed 

while showering. Section 3.4.1 provides an overview of the conceptual model, including the 

basic approach and assumptions. Section 3.4.2 discusses the input parameters and values used in 

this risk analysis. Section 3.4.3 discusses the model outputs. Appendix H provides additional 

information about the shower model: 

 Appendix H, Equations, presents the equations that were used to estimate shower air 

exposures. 

3.4.1 Conceptual Model 

When present, residential groundwater wells typically serve as a household’s primary 

water supply, which is used for showering, as well as other general purposes. Because the 

solvents considered in this analysis are volatile, the potential inhalation exposure associated with 

constituent volatilization that occurs during showering is of particular concern for the 

groundwater pathway. 

3.4.1.1 Overview 

The shower model calculates the average air concentration of a contaminant to which the 

receptor is exposed associated with showering, including both time spent in the shower and time 

spent in the bathroom immediately after a shower. Exposure to solvents released during 

showering was evaluated for adult residents only. Young children were assumed primarily to 

take baths and thus not encounter significant exposure to solvents released while showering. 

The shower methodology implemented in this analysis is consistent with those applied in 

several previous risk analyses, including the Dye and Pigment risk analysis (U.S. EPA, 2003b). 

Appendix H presents the algorithms that form the basis of the shower model. The exposure 

factors used in the shower model, most of which are from U.S. EPA Exposure Factors 

Handbook (U.S. EPA (1997e), are documented in Appendix I. The times spent showering, in the 

shower stall after showering, and in the bathroom were varied; the rest were fixed. 

The shower model estimates the change in the shower and bathroom air concentrations 

over time based on the mass of constituent lost by the water and the air exchange rate between 

the shower, the bathroom, and the rest of the house. The shower model used for this analysis is 

based McKone (1987) and Little (1992a). The differential equations were solved using finite 

difference numerical integration. 

The shower model estimates the concentration of a constituent in shower and bathroom 

air that results from the mass transfer of the constituent from water to shower air. The following 

equation estimates the overall mass transfer coefficient from tap water to air from showering: 

1

3/23/2

15.2 −

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

′ +×=

HDD

K

w a

ol β (Equation 3-3) 

where 

Kol = overall mass transfer coefficient (cm/s) 

β = proportionality constant (cm-s)-1/3 

Do not cite or quote. 3-43 
Review Draft Section 3.0 Analysis 

Dw = diffusion coefficient in water (cm2/s) (contaminant specific, provided in 

Appendix B) 

Da = diffusion coefficient in air (cm2/s) (contaminant specific, provided in Appendix B) 

H’ = dimensionless Henry’s law constant (calculated using the equation in Appendix H). 

Equation 3-3 was presented in McKone (1987) with the proportionality constant, β, noted 

as a dimensionless value. In a correspondence to the editor, Little (1992b) indicated that β is not 

dimensionless, but has units of (cm-s)-1/3 as noted above. Because McKone did not provide an 

estimate for β, a value was derived using data for benzene. The modeled results using the 

benzene-derived value, 216 (cm-s)-1/3, compared favorably to experimental data for several 

organic compounds of varying volatility (Coburn, 1996). 

The constituent emission rate was estimated from the change in the shower water 

concentration as the water falls, which is calculated using the overall mass transfer coefficient as 

follows: 

⎟

⎠

⎞ ⎜

⎝

⎛ − ⎟

⎠

⎞ ⎜

⎝

⎛ −= ' H

yC

V

A K

dt

dC s

ol (Equation 3-4) 

where 

C = liquid-phase (droplet) constituent concentration (μg/cm3 or mg/L) 

t = time (s) 

Kol = overall mass transfer coefficient (cm/s) 

A = total surface area for mass transfer (cm2) 

V = total volume of water within the shower compartment (cm3) 

ys = gas-phase constituent concentration in the shower (μg/cm3 or mg/L) 

H́’ = dimensionless Henry’s law constant (calculated using the equation in Appendix H). 

Consequently, in addition to the overall mass transfer coefficient, the emission rate of a 

contaminant within the shower is dependent on the surface-area-to-volume ratio of the shower 

water and the concentration driving force between the water and the shower air. Equation 3-4 

above is equivalent to Equation 4 presented in Little (1992a). 

The shower emissions were modeled based on falling droplets as a means of estimating 

the surface-area-to-volume ratio for mass transfer and the residence time of the water in the 

shower compartment. Equation 3-4 was integrated assuming the constituent concentration in the 

gas phase, ys, is constant over the timeframe of the droplet fall. The time required for a droplet to 

fall equals the distance traveled (nozzle height) divided by the water droplet velocity. The ratio 

of the surface-area-to-droplet volume (A/V) equals 6/dp (assuming a spherical shape where d is 

droplet diameter). Given the assumption that the drops fall at terminal velocity, the surface-areato-volume ratio and the residence time were based solely on droplet size. A droplet diameter of 

approximately 1 mm (0.098 cm) was selected. The terminal velocity for the selected droplet size 

was approximately 400 cm/s. The fraction of constituent emitted from a water droplet at any 

given time was calculated by integrating Equation 3-4 and rearranging as follows: 

( ) ( N

sat

in

out

sem ef C

C f − −−= − = 11 1 , ) (Equation 3-5) 

where 

fem,s = fraction of constituent emitted from the shower droplet (unitless) 

Do not cite or quote. 3-44 
Review Draft Section 3.0 Analysis 

Cout = droplet constituent concentration at shower floor/drain (mg/L) 

Cin = droplet constituent concentration entering the shower (mg/L) 

fsat = fraction of gas-phase saturation (unitless; calculated using the equation in 

Appendix H) 

N = dimensionless overall mass transfer coefficient (calculated using the equation in 

Appendix H). 

Equation 3-5 is equivalent to Equation 5 in Little (1992a) for liquid-phase concentration 

change, except that Little used the total surface area for mass transfer (lumped with Kol) and the 

shower flow rate to calculate an overall mass transfer coefficient, where N = (Kol×A)/QL) and QL

is the volumetric flow rate of the water. However, the surface area of the droplet was a difficult 

value to obtain, so the equation for N was rearranged as N = (Kol)(A/V)(DropRT), where the drop 

residence time (DropRT) represents the time it takes for a droplet to fall. This eliminated the 

need for total surface area. The drop residence time was calculated using the height and the 

velocity of the droplet (h/vt), and the area-to-volume ratio (A/V) was set to 6/dp (as described 

above, see also Appendix H). 

After calculating the fraction of constituent emitted from the shower water, the model 

converts the fraction to a mass emitted for each time step. The mass was calculated by 

multiplying the total mass entering the shower during a given time step by the fraction emitted. 

The total mass entering the shower was determined by multiplying the concentration in the 

shower water by the flow rate of the shower head and the length of the time step. A time step of 

0.2 minutes was used in the model. The gas-phase constituent concentration in the shower was 

calculated for each time step for the duration of the shower. The air exchange rate between the 

shower and the bathroom was included in the estimation of the gas-phase concentration of the 

solvents in the shower, as follows: 

[ ( ( ) )] y y

E Q y y ts

V s, t ts s, t

sb s, t b, t

s

+ = +

−×− ×

× 1000 (Equation 3-6) 

where 

ys, t+ts = gas-phase constituent concentration in the shower at the end of time step (mg/L) 

ys, t = gas-phase constituent concentration in the shower at the beginning of time step 

(mg/L) 

Qsb = volumetric gas exchange rate between shower and bathroom (L/min) 

yb, t = gas-phase constituent concentration in the bathroom at the beginning of time step 

(mg/L) 

ts = time step (min) = 0.2 

E = mass of constituent emitted from shower during time step (mg) 

Vs = volume of shower stall (m3) 

 1000 = conversion factor (L/m3). 

The shower model also estimates bathroom exposure. The gas-phase constituent 

concentration in the bathroom was estimated as follows for each time step of the exposure 

duration: 

[ ] ( ) ( ) ( )

y y

Qy y Qy y t

V b t ts b t

sb s t ts b t bh b t h t

b

, ,

, , ,,

+

+

= +

× − −× − ×

× 1000

s

 (Equation 3-7) 

Do not cite or quote. 3-45 
Review Draft Section 3.0 Analysis 

where 

yb, t+ts = gas-phase constituent concentration in the bathroom at end of time step (mg/L) 

yb, t = gas-phase constituent concentration in the bathroom at beginning of time step 

(mg/L) 

Qbh = volumetric gas exchange rate between bathroom and house (L/min) 

Qsb = volumetric gas exchange rate between shower and bathroom (L/min) 

ys, t+ts = gas-phase constituent concentration in the shower at end of time step (mg/L) 

yh, t = gas-phase constituent concentration in the house at beginning of time step (mg/L) 

ts = time step (min) = 0.2 min 

Vb = volume of bathroom (m3) 

 1000 = conversion factor (L/m3). 

The gas-phase constituent concentration in the house was assumed to be negligible in 

comparison with the gas-phase constituent concentration in the bathroom. Therefore, the gasphase concentration of the constituent in the house (yh, t) was set to zero. 

The average air concentrations in the shower and bathroom were calculated by averaging 

the concentrations from each time step over the duration of the shower and bathroom use. From 

the average shower concentration and the average bathroom concentration, a time-weighted 

average concentration for an entire day was calculated. This time-weighted concentration was 

used to estimate chronic health impacts due to inhalation exposures from showering. 

3.4.1.2 Key Assumptions 

The key assumptions, uncertainties, and limitations associated with the shower model 

that are not addressed elsewhere in this document are as follows: 

 Adult residents are assumed to take one shower per day. 

 The shower model assumes the specified frequency and duration of exposure (showers 

350 days/year lasting 1 to 60 minutes each). 

 The shower model assumes the mathematical formulation of McKone (1987) and Little 

(1992a). 

3.4.2 Model Inputs 

The shower model requires inputs on exposure parameters specific to shower exposure 

(e.g., time spent showering, time spent in the bathroom after showering), shower properties (e.g., 

shower and bathroom volume, nozzle height, flow rate), droplet properties (diameter and 

velocity), and air exchange rates (e.g., between shower and bathroom). The exposure factors 

were represented with distributions, based on data in the Exposure Factors Handbook (U.S. EPA 

1997e). For shower properties, we used fixed midrange values reported in the literature. 

Although fixing shower parameters does not capture variability in these inputs when the model is 

run, the results compare favorably to experimental data for numerous organic compounds of 

varying volatility (Coburn, 1996). The droplet properties (diameter and velocity) were also held 

constant, with their values based on a correlation to existing data. The volumetric exchange rates 

were derived from McKone (1987). The values used (300 L/min for the exchange rate between 

the bathroom and house, and 100 L/min for the exchange rate between the shower and bathroom) 

fall within the range of values reported in a five-study summary (Little, 1992a), which showed 

ranges of 35 to 460 L/min for the exchange between the shower and bathroom and 38 to 480 

L/min for the exchange between the bathroom and the rest of the house. 

Do not cite or quote. 3-46 
Review Draft Section 3.0 Analysis 

3.4.3 Model Outputs 

The outputs from the model are time-weighted average concentrations for an entire day, 

which were used to estimate chronic health impacts due to inhalation exposures from showering. 

As discussed above, the time-weighted average concentration for an entire day was calculated 

from the average shower concentration and the average bathroom concentration. These values 

were calculated by averaging the concentrations from each time step over the duration of the 

shower and bathroom use. 

3.5 Exposure Modeling 

This section describes the methodology and the model that was used to predict human 

exposure to chemical constituents after their release from a landfill into ambient air and 

groundwater. Section 3.5.1 provides an overview of the conceptual model, including the basic 

approach and assumptions. Section 3.5.2 discusses the input parameters and values used in this 

risk analysis. Section 3.5.3 discusses the model outputs. Appendices H and I, listed below, 

provide additional information about the exposure model and inputs: 

 Appendix H, Equations, presents the equations that were used to model human exposure. 

 Appendix I, Human Exposure Factors, presents the exposure factors used as input to the 

model. 

3.5.1 Conceptual Model 

Human exposure modeling was conducted to estimate the magnitude, frequency, and 

duration of an individual’s exposure to solvents from contaminated wipes and laundry sludges in 

landfills. The term “exposure” refers to the condition that occurs when a contaminant comes into 

contact with the outer boundary of the body (see EPA guidelines for exposure assessment, 57 FR 

22888-22893). An individual’s exposure to a contaminant completes an exposure pathway (i.e., 

the course a constituent takes from the landfill to an individual). Once the body has been 

exposed, the constituent can cross the outer boundary and enter the body. 

3.5.1.1 Overview 

Exposure Pathways 

Human receptors may come into contact with solvents released from a landfill through a 

variety of pathways. The three exposure pathways included in the analysis were identified based 

on the solubility and volatility of the solvents being considered: 

 Inhalation of Ambient Air. Both vapors and particles can be inhaled in ambient air by a 

receptor, but given the physical characteristics of the solvents modeled, only vapors were 

considered in this analysis. 

 Inhalation of Indoor Air During Showering. Groundwater can be contaminated from 

solvents leaching from the landfill into the vadose zone and migrating into the 

groundwater. Groundwater from an offsite well was assumed to be used in the residence 

for showering by the adult residential receptor. Constituents can volatilize from shower 

water, thus resulting in inhalation exposures. 

Do not cite or quote. 3-47 
Review Draft Section 3.0 Analysis 

 Ingestion of Drinking Water. In addition to inhalation of indoor air released during 

showering, ingestion of contaminated drinking water from groundwater sources was 

considered. Groundwater from an offsite well was assumed to be used as a source of 

drinking water for residents. 

 Dermal Exposure During Showering/Bathing. Dermal exposure while showering or 

bathing in contaminated groundwater was also considered. Groundwater from an offsite 

well was assumed to be used in the residence for showering (by the adult residential 

receptor) or bathing (by the child residential receptor). 

Exposure via groundwater pathways (i.e., ingestion of drinking water, inhalation during 

showering, and dermal exposure during showering or bathing) was considered separately from 

exposure to ambient air in this analysis because the exposure time scales differ and because the 

groundwater plume occurs in a limited area relative to potential ambient air receptor locations. 

For instance, a significant fraction of the mass that will be released into the air from landfill 

operations will be released during the landfill’s operating life (generally between 40 and 75 

years); in contrast, significant constituent leaching from the bottom of a landfill and transport 

through the saturated zone to a receptor well is likely to occur on the order of hundreds to 

thousands of years. Furthermore, receptors for the ambient air pathway were located based on 

random selection of distance and direction, which may not necessarily coincide with the location 

of the groundwater plume or contaminated wells. 

Receptors 

Adult and child residents were modeled as receptors in this analysis. Table 3-10 shows 

the specific exposure pathways that were modeled for each type of receptor. Both adult and child 

residents were modeled for exposure via inhalation of ambient air, ingestion of groundwater, and 

dermal exposure during showering (adults) or bathing (children). Only adult residents were 

modeled for exposure to the groundwater pathway via the inhalation of vapors generated during 

showering with well water; children were assumed to take baths, not showers. 

Table 3-10. Receptors by Exposure Pathway 

Receptor 

Inhalation of 

Ambient Air 

Ingestion of 

Drinking Water 

Inhalation of 

Indoor Air 

(Shower)a

Dermal Exposure 

to Groundwater 

(Shower/Bath) 

Adult resident Υ Υ Υ Υ 

(shower) 

Child resident Υ Υ Υ 

(bath)

a Showering is explicitly considered for adults and not for children; however, the results can be considered to bracket the risk for 

this pathway. In terms of noncancer risk, the hazard quotient is calculated based on the air concentrations, which are the same

regardless of age, and the time spent in the shower, which is based on data for all ages. For cancer, adults are typically the 

most sensitive receptor because of the longer exposure. 

Children are an important subpopulation to consider in a risk analysis because they are 

likely to be more heavily exposed than adults; for example, compared to adults, children may 

drink more fluids per unit of body weight. This higher intake-rate-to-body-weight ratio can result 

in a higher ADD than adults’ experience. As children mature, their physical characteristics and 

Do not cite or quote. 3-48 
Review Draft Section 3.0 Analysis 

behavior patterns change. To capture these changes in the analysis, the life of a child was 

considered in stages represented by the following cohorts: cohort 1 (ages 1–5), cohort 2 (ages 6–

11), cohort 3 (ages 12–19), and cohort 4 (ages 20–70, adult). Associated with each cohort are 

distributions of values, called exposure parameters that are required to calculate an individual’s 

exposure. The exposure parameter distributions for each cohort reflect the physical 

characteristics and behavior patterns of the individuals in that age range. Distributions for each 

exposure factor appropriate for each cohort were derived; the distributions for cohort 4 (ages 20–

70) were used for the adult receptor. For all exposure factors except the child bathing duration, 

the distributions were based on data from the Exposure Factors Handbook (EFH, U.S. EPA, 

1997c,d). The child bathing duration was based on data from the review draft of the ChildSpecific Exposure Factors Handbook (Child-Specific EFH, U.S. EPA, 2006d), because the data 

on this exposure factor from the EFH reflects total bath/shower time, which would include 

activities such as drying, while the data from the Child-Specific EFH on this factor reflects just 

time in the bathtub. 

The development of the child exposure parameters consisted of two steps: (1) defining 

the start age of the child and (2) selecting the exposure duration of the child. To capture the 

children’s higher intake-rate-to-body-weight ratio, a start age of 1 was selected. The distribution 

of exposure duration for cohort 1 (ages 1–5) was used to define the child’s exposure duration for 

each of the 10,000 iterations in the probabilistic analysis. 

Estimation of Dose 

The model estimates the dose to each receptor by combining exposure parameters with 

media concentrations. Estimates of exposure were based on the potential dose (e.g., the dose 

ingested) rather than the applied dose (e.g., the dose delivered to the gastrointestinal tract) or the 

internal dose (e.g., the dose delivered to the target organ). This is generally consistent with the 

exposure metric used in the toxicological benchmarks used for risk analysis (see Section 3.6). 

The approaches used to estimate groundwater ingestion dose and dermal dose are 

provided below. Doses were not calculated for inhalation exposures, because the inhalation 

cancer risk equation (Equation 3-14 in Section 3.6) and the inhalation HQ equation (Equation 

3-17 in Section 3.6) calculate directly from air concentration. 

Doses from groundwater ingestion were calculated by multiplying the contaminant 

concentration by the respective intake rate on a per-kilogram-of-body-weight basis. Doses were 

then summed for the period of time during which exposure occurred, resulting in an ADD 

received from ingestion exposure. The ADD was used to assess noncancer risk from ingestion of 

groundwater. For cancer effects, where the biological response is described in terms of lifetime 

probabilities even though exposure may not occur over the entire lifetime, dose is presented as an 

LADD. The LADD was used to assess cancer risks from ingestion of groundwater. For the 

purposes of this analysis, the ADD of contaminant from the consumption of drinking water was 

calculated as follows: 

×1000

× × = BWFCRC ADD dw dw dw

dw (Equation 3-8) 

where 

ADDdw = average daily dose (mg/kg-d) 

 Cdw = average concentration in drinking water (mg/L) 

CRdw = consumption rate of drinking water (mL/day) 

Do not cite or quote. 3-49 
Review Draft Section 3.0 Analysis 

Fdw = fraction of drinking water that is contaminated (unitless) 

BW = body weight (kg) 

 1000 = conversion factor (mL/L). 

The LADD used for assessing risks for carcinogenic effects from consumption of 

contaminated drinking water was calculated as follows: 

× 365

× × = ATADD EFED

LADD dw (Equation 3-9) 

where 

LADD = lifetime average daily dose (mg/kg-d) 

ADDdw = average daily dose (mg/kg-d) 

ED = exposure duration (yr) 

EF = exposure frequency (d/yr) 

AT = averaging time (yr) 

 365 = conversion factor (d/yr). 

Doses from dermal contact with groundwater while showering or bathing were estimated 

and evaluated for both the child and adult resident. Dermal dose and risk estimates were 

developed based on guidance provided in Risk Assessment Guidance for Superfund Volume I: 

Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk 

Assessment) Interim (RAGS Part E; U.S. EPA; 2004b). Consistent with this guidance, the 

exposure scenario applied for the child was a bathing event and for the adult, a showering event. 

Appendix H presents the algorithms that form the basis of the dermal exposure and risk 

modeling. The exposure factors used in the dermal dose estimates are from RAGS Part E, the 

EFH (U.S. EPA, 1997e), and the Child-Specific EFH (U.S. EPA, 2006c) and are documented in 

Appendix I. The chemical-specific parameters used in the calculations are presented in 

Appendix B. 

For the purposes of this analysis, the DAD of contaminant from dermal exposure during 

showering or bathing was calculated for carcinogens as follows: 

ATBW

SAEFEDEVDA DAD event

×

×××× = (Equation 3-10) 

where 

DAD = dermal absorbed dose (mg/kg-d) 

 DAevent = absorbed dose per event (mg/cm2

-event) 

EV = event frequency (events/day) 

ED = exposure duration (years) 

EF = exposure frequency (days/year) 

 SA = skin surface area available for contact (cm2) 

BW = body weight (kg) 

 AT = averaging time (years). 

Body surface area and body weight are strongly correlated (0.986; U.S. EPA, 1997e), and 

the EFH discusses that the use of the ratio of body surface area to body weight (SA/BW) can be 

more appropriate than treating these factors as independent variables in conducting human 

exposure assessments. Therefore, we used data on SA/BW in Equation 3-10 (replacing SA in the 

numerator with SA/BW and eliminating BW in the denominator). 

Do not cite or quote. 3-50 
Review Draft Section 3.0 Analysis 

For noncarcinogens, it was assumed that the exposure duration, exposure frequency, and 

averaging time terms would cancel, resulting in the following simplified equation for 

noncarcinogens:16

BW

SAEVDA DAD event × × = (Equation 3-11) 

3.5.1.2 Key Assumptions 

Exposure modeling relies heavily on default assumptions concerning population activity 

patterns, mobility, dietary habits, body weights, and other factors. This section presents key 

assumptions associated with the applied modeling approach. 

 Solvent exposure through the inhalation of ambient air was assessed separately from 

exposures to solvents in groundwater (via ingestion of drinking water, inhalation of 

volatile solvents during showering, or dermal exposure to solvents during showering or 

bathing) due to assumed temporal and spatial differences in the exposures. 

 Receptors were assumed to both live and work at the receptor location identified for them 

during site characterization. In some cases, this assumption may over-estimate 

exposures, such as when an individual lives at the identified location within the study 

area but commutes to work, school or daycare outside of the study area. Alternately, this 

assumption might under-estimate some exposures, such as when an individual lives in a 

less-contaminated part of the study area, but commutes to a more contaminated part of 

the study area. 

 For children, a start age of 1 year was selected. 

 Showering was considered for adults and not for children; however, the results can be 

considered to bracket the exposure for this pathway. For noncancer effects, the HQ was 

calculated based on the air concentrations, which are the same regardless of age, and the 

time spent in the shower, which is based on data for all ages. For cancer effects, adults 

are typically the most sensitive receptor because of the longer exposure. 

 For most of the human exposure factors, distributions were fitted to the data summaries 

from the EPA’s EFH. The approach most often taken was to fit these distributions to 

selected percentiles reported in the EFH rather than to the raw data. This approach 

implies that fitting to percentiles will produce essentially the same distribution as fitting 

to raw data. 

 The chi-square goodness-of-fit test was used to select the most appropriate statistical 

distribution for most of the exposure factors. However, all of the statistics that were used 

to assess goodness of fit assume a random sample, which may or may not be a valid 

assumption for data presented in the EFH, because those data are based on surveys that, 

in many cases, did not involve purely random samples (e.g., they used clustering and 

stratification, primarily for economic reasons). For a national Monte Carlo simulation, 

this is considered to be a relatively minor—albeit unquantified—source of uncertainty. 

 

16 As stated in RAGS Part E (U.S. EPA, 2004b), averaging time for noncarcinogens is equal to exposure duration expressed in 

days (ED x 365 d/yr). Assuming an exposure frequency for noncarcinogens of 365 d/yr, the terms ED, EF, and AT 

cancel. 

Do not cite or quote. 3-51 
Review Draft Section 3.0 Analysis 

 The modeling approach used to estimate dose via shower inhalation and dermal contact 

did not account for solvent mass lost in one of these exposure routes when calculating the 

other. This approach will result in an overestimation of dose. 

3.5.2 Model Inputs 

The inputs to the exposure model are human exposure factors and media concentrations. 

Media concentrations for ambient air, groundwater, and indoor air (i.e., shower and bathroom) 

are discussed in Sections 3.2, 3.3, and 3.4, respectively. The exposure factors used as input to the 

model are discussed generally below and presented in Appendix I. 

Table 3-11 lists the exposure factors used in this analysis, along with the data sources 

and whether the data were represented as a distribution or a fixed value in the Monte Carlo 

analysis. The approach used in determining these distributions and fixed values is consistent with 

the methodology applied in several previous EPA risk analyses, including the Dye and Pigment 

Listing risk analysis (RTI, 2003), which was extensively reviewed by independent experts and 

EPA’s Science Advisory Board. 

Table 3-11. Human Exposure Factor Input Parameters and Data Sources 

Parameter Variable Type Data Source 

Body weight (adult, child) Distribution U.S. EPA (1997c) 

Ingestion rate: drinking water (adult, child) Distribution U.S. EPA (1997c) 

Time spent in shower stall after showering (adult) Distribution U.S. EPA (1997c) 

Time spent in bathroom immediately following shower 

(adult) 

Distribution U.S. EPA (1997c) 

Shower contact time (adult) Distribution U.S. EPA (1997e) 

Bathing contact time (child) Distribution U.S. EPA (2006c) 

Ratio of surface area to body weight (child) Distribution U.S. EPA (1997c) 

Exposure duration (adult, child) Distribution U.S. EPA (1997e) 

Exposure frequency (adult, child) Fixed (constant) Assumed value 

Fraction contaminated: drinking water Fixed (constant) Assumed value 

Event frequency: showering and bathing (adult and child) Fixed (constant) U.S. EPA (1997e) 

Human lifetime (used in carcinogenic risk calculation) Fixed (constant) Assumed value

 Body Weights. Distributions of body weight were developed for adult and child 

receptors based on EFH data. 

 Drinking Water Ingestion Rates. Distributions of drinking water ingestion rates were 

developed for adult and child receptors based on EFH data. 

 Showering/Bathing Parameters. The time spent in shower stall after showering, times 

spent in bathroom immediately following shower, and the shower contact time were used 

in the calculation of shower inhalation exposure and were based on EFH data. The 

shower contact time was also used to estimate the DAD for the adult. The bathing contact 

time, from the Child-Specific EFH, was used to estimate the DAD for the child. 

Do not cite or quote. 3-52 
Review Draft Section 3.0 Analysis 

 Ratio of Surface Area to Body Weight. The ratio of surface area to body weight was 

used in the calculation of the DAD for both the adult and child receptors and was based 

on EFH data. 

 Exposure Duration. Exposure duration refers to the amount of time that a receptor is 

exposed to a contaminant source. For this analysis, exposure duration was assumed to 

correspond with the receptor’s residence time in the same house. Exposure durations 

were determined using EFH data on residential occupancy (U.S. EPA, 1997e). Because 

the data used to develop parameter information for resident receptors were age specific, 

separate distributions were developed for adult and child residents. Exposure durations 

used in this analysis are provided in Appendix I. The exposure duration was used to 

determine the length of time over which a person was exposed. In addition, exposure 

duration for each realization was used to calculate a time-weighted average concentration 

in the contact medium (e.g., groundwater) to ensure that the length of exposure and the 

constituent concentration to which a person is exposed were consistent. 

 Exposure Frequency. Exposure frequency is the frequency at which the receptor is 

exposed to the contaminated source during the exposure duration. Unlike exposure 

duration, which accounts for the amount of time the receptor spends at the point of 

exposure, the exposure frequency simply indicates the number of days that the receptor is 

assumed to be at that location (versus some other location, such as another city or state). 

Exposure frequency was not assumed to vary, so distributions were not developed for 

exposure frequency. All receptors were assumed to be exposed to the contaminant source 

350 days a year. This value is based on an assumption that individuals are away from 

their homes (e.g., on vacation) approximately 2 weeks each year but are otherwise in the 

receptor area for 24 hours per day. 

 Lifetime and Averaging Time. Averaging time is the period of time over which a 

receptor’s dose is averaged. To evaluate carcinogens, total dose was averaged over the 

lifetime of the individual, which was assumed to be 70 years, to calculate the LADD (see 

Equation 3-9). To evaluate noncarcinogens, the ADD was used, which requires neither 

exposure duration nor averaging time (see Equation 3-8). 

These exposure factors were used to calculate the dose (for ingestion and dermal 

pathways) or risk (for inhalation pathways) based on water or air concentration. The primary 

data source of human exposure model inputs used in this analysis was the EFH. The EFH 

summarizes data on human behaviors and characteristics related to human exposure from 

relevant key studies and provide recommendations and associated confidence estimates on the 

values of exposure factors. These data were carefully reviewed and evaluated for quality before 

being included in the EFH. EPA’s evaluation criteria included peer review, reproducibility, 

pertinence to the United States, currency, adequacy of the data collection period, validity of the 

approach, representativeness of the population, characterization of the variability, lack of bias in 

study design, and measurement error (U.S. EPA, 1997c,d). 

For probabilistic risk analyses, PDFs are developed based on the values in the EFH. 

Appendix I presents the exposure factors used in this analysis. These factors are the same as 

those applied in the Dye and Pigment Listing risk analysis (U.S. EPA, 2003b). Appendix I also 

describes the rationale and data used to select the parametric models (i.e., gamma, lognormal, 

and Weibull) for those exposure factors that were varied in the probabilistic analysis. 

Do not cite or quote. 3-53 
Review Draft Section 3.0 Analysis 

3.5.3 Model Outputs 

The outputs from the exposure model are receptor-specific estimates of exposure based 

on the potential dose ingested or dermally absorbed. These dose estimates were used as input to 

the human health effects model. The LADD was used to assess cancer risks from ingestion 

exposure routes. The ADD was used to assess noncancer risk from ingestion exposures. The 

DAD was used to assess cancer risk and noncancer hazard from dermal exposures. Inhalation 

cancer and noncancer risks were evaluated using ambient air concentrations or shower air 

concentrations. 

3.6 Human Health Effects Modeling 

This section presents the human health benchmarks and approach used to estimate 

potential health risks in the modeling simulation. Section 3.6.1 provides an overview of the 

conceptual model, including the basic approach and assumptions. Section 3.6.2 discusses the 

input parameters and values used in this risk analysis. Section 3.6.3 discusses the model outputs. 

The risk equations used in the human health effects modeling are presented in Appendix H. 

3.6.1 Conceptual Model 

Human health effects modeling was performed to estimate cancer and noncancer health 

impacts due to direct inhalation of ambient air, ingestion of groundwater contaminated by 

leachate, inhalation of shower vapors, and dermal exposure to contaminated groundwater during 

showering or bathing. A chemical constituent’s ability to cause an adverse health effect depends 

on the toxicity of the particular constituent, the route of exposure to an individual (ingestion or 

inhalation), the duration and intensity of exposure, and the resulting dose that an individual 

receives. In this analysis, estimates of toxicity (the human health benchmarks) and exposure 

doses or exposure concentrations were combined to calculate individual excess lifetime 

carcinogenic risk estimates and noncancer HQs (Section 3.6.1.1). The toxicity of a constituent 

was defined in terms of constituent-specific human health benchmarks for each route of exposure 

for cancer and noncancer endpoints. The health benchmarks used in this analysis are point 

estimates of carcinogenicity and noncancer toxicity, respectively, that were used to quantify the 

cancer risk and noncancer hazard for chronic exposures predicted during the Monte Carlo 

simulation. The benchmarks were derived from toxicity studies on cancer and noncancer 

endpoints in animals, as well as human epidemiological studies. 

Human health benchmarks for chronic exposures were used in this analysis to 

characterize the potential cancer risks and noncancer hazards associated with two management 

scenarios (lined and unlined landfills). Oral cancer slope factors (CSFs) and inhalation unit risk 

factors (URFs) were used to produce risk estimates for carcinogens. Reference doses (RfDs) and 

reference concentrations (RfCs) were used to estimate the noncancer hazards from oral and 

inhalation exposures, respectively. For dermal exposures, in the absence of chemical-specific 

absorption data, we conservatively assumed 100 percent absorption per guidance in RAGS Part 

E (U.S. EPA, 2004b) and used the oral CSF and RfD to estimate the cancer risks and noncancer 

hazards, respectively, from dermal contact with contaminated groundwater. 

Do not cite or quote. 3-54 
Review Draft Section 3.0 Analysis 

Human health benchmarks serve as the risk metrics related to the risk criteria presented in

Section 1.0, and in essence, determine how risks are interpreted when potential health effects are 

assessed. As discussed in Section 5.0, the solvent-specific mass loadings were defined as loading 

limits that result in cancer risks estimated to be in the range of 1 chance in 10,000 to 1 chance in 

1,000,000 and noncancer HQs estimated to be 1. Specifically, the risk metrics for this analysis 

for individual exposure pathways were as follows: 

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance 

in 1 million for adults and children due to the inhalation of ambient air (based on a URF) 

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance 

in 1 million for adults due to the combined exposures resulting from ingestion of drinking 

water, inhalation of indoor air during showering, and dermal contact with groundwater 

during showering, for residents on groundwater wells. The cancer risk is based on the 

oral CSF for ingestion and dermal exposures and on the URF for inhalation exposure. 

 Excess lifetime cancer risk estimated to be in the range of 1 chance in 10,000 to 1 chance 

in 1 million for children due to the combined exposures resulting from ingestion of 

drinking water and dermal contact with groundwater during bathing, for residents on 

groundwater wells. The cancer risk is based on the oral CSF for ingestion and dermal 

exposures and on the URF for inhalation exposure. 

 A ratio of 1 of the ambient air concentration to the noncancer benchmark (RfC) for adults 

and children due to the inhalation of ambient air 

 A ratio of 1 of the estimated daily dose to the noncancer benchmark (RfD) for adults due 

to the combined exposures resulting from ingestion of drinking water, inhalation of 

indoor air during showering, and dermal contact with groundwater during showering, for 

residents on groundwater wells 

 A ratio of 1 of the estimated daily dose to the noncancer benchmark (RfD) for children 

due to the combined exposures resulting from ingestion of drinking water and dermal 

contact with groundwater during bathing, for residents on groundwater wells 

 For noncarcinogens with the same target organ and endpoint for both inhalation and 

ingestion exposure, an HQ equal to 1 for adults and children on groundwater wells due to 

the ingestion of drinking water (adult and child), inhalation of indoor air during 

showering (adult only), and dermal contact during showering (adult) or bathing (child). 

The HQ is based on the ratio of the predicted dose or air concentration to the appropriate 

health benchmark for noncancer endpoints, the RfD for ingestion and dermal exposure 

and the RfC for inhalation exposure. 

3.6.1.1 Overview 

As discussed in Section 3.5.1.1, the timeframe for environmental releases of chemical 

constituents from landfills is very different for air and groundwater. Consequently, potential risks 

from the inhalation of ambient air contaminated with solvents and the exposure to contaminated 

groundwater were not combined. 

For the groundwater pathway, the cancer risks were combined for drinking water 

ingestion, inhalation during showering, and dermal exposure during showering for adults, and for 

drinking water ingestion and dermal exposure during bathing for children, provided that no 

Do not cite or quote. 3-55 
Review Draft Section 3.0 Analysis 

portal-of-entry effect was reported for the specific chemical solvent. For solvents with both an 

oral CSF and an inhalation URF considered in this analysis, no portal-of-entry effects were 

reported, and therefore, total cancer risks were calculated. For noncarcinogens, the noncancer 

hazards were combined for the three pathways only for solvents with inhalation and ingestion 

health endpoints indicating that the target organ and effect was independent of the exposure 

route. This was the case for benzene, cresol (mixture), and methyl ethyl ketone. 

Cancer Risk 

Cancer risk was characterized by estimating the lifetime excess cancer risk, representing 

the increased probability of developing cancer over a lifetime as a result of exposure to the 

solvents. For the drinking water ingestion pathway, the lifetime excess cancer risk estimates 

were based on the LADD (see Equation 3-9) and the oral CSF: 

Riskoral = LADD ×CSFo (Equation 3-12) 

where 

Riskoral = lifetime incremental cancer risk from the oral exposure route (unitless) 

LADD = lifetime average daily dose (mg/kg-d) 

CSFo = oral cancer slope factor (mg/kg-d)-1.

For the groundwater dermal contact pathway, the lifetime excess cancer risk estimates 

were based on the DAD for cancer endpoints (see Equation 3-10) and the oral CSF: 

Riskdermal = ×CSFDAD o (Equation 3-13) 

where 

Riskoral = lifetime incremental cancer risk from the oral exposure route (unitless) 

DAD = dermal absorbed dose (mg/kg-d) 

CSFo = oral cancer slope factor (mg/kg-d)-1. 

For inhalation of ambient air and inhalation of indoor air during showering, lifetime 

excess cancer risk estimates were calculated as follows: 

AT

URFC ED Risk air

air

× × × = 1000

 (Equation 3-14) 

where 

Riskair = lifetime incremental cancer risk from the air exposure route (unitless) 

Cair = concentration in air (mg/m3) 

URF = inhalation unit risk factor ([μg/m3

]

-1) 

 1000 = conversion factor (μg/mg) 

ED = exposure duration (yr) 

AT = averaging time (yr). 

Solvent-specific individual lifetime excess cancer risks were generated for each receptor 

for inhalation, ingestion, and dermal pathway exposures, and as suggested by the risk metrics, 

the pathway-specific lifetime excess cancer risks for the groundwater pathway were summed to 

generate a total lifetime excess cancer risk for adults. As noted above, risks due to inhalation of 

ambient air were not summed with the groundwater pathway risks because these exposures were 

not predicted to occur in the same timeframe. 

Do not cite or quote. 3-56 
Review Draft Section 3.0 Analysis 

Noncancer Hazard 

Noncancer hazard from drinking water ingestion was characterized based on the ADD 

during the period of exposure (see Equation 3-8) and the noncancer benchmark for the ingestion 

route (RfD): 

RfD

ADD

HQ dw

oral = (Equation 3-15) 

where 

 HQoral = oral hazard quotient (unitless) 

ADDdw = average daily dose from consumption of drinking water (mg/kg-d) 

RfD = reference dose (mg/kg-d). 

This ratio is referred to as an HQ, and as a risk metric, is used to establish a threshold of concern 

for a specific health effect. Unlike cancer risk estimates, HQs are not probability statements; 

rather, the RfD represents a daily exposure that is likely to be without appreciable risk of 

deleterious noncancer effects during a lifetime. 

Noncancer hazard from dermal exposure via showering or bathing was characterized 

based on the DAD during the period of exposure (see Equation 3-11) and the noncancer 

benchmark for the ingestion route (RfD): 

RfD

DAD HQdermal = (Equation 3-16) 

where 

 HQoral = oral hazard quotient (unitless) 

DAD = average daily dose from consumption of drinking water (mg/kg-d) 

RfD = reference dose (mg/kg-d). 

Noncancer hazard from air inhalation was based on the average air concentration during 

the period of exposure and the noncancer benchmark for the inhalation route (RfC): 

RfC

C HQ air

air = (Equation 3-17) 

where 

 HQair = air hazard quotient (unitless) 

Cair = air concentration (mg/m3

) 

RfC = reference concentration (mg/m3). 

As with noncancer effects for the ingestion pathway, this ratio is also an HQ and defines 

an estimate that is likely to be without adverse effects. 

As with the carcinogens, hazards due to inhalation of ambient air and inhalation of indoor 

air during showering were not summed because these exposures were not predicted to occur in 

the same timeframe. Noncancer hazard estimates from groundwater ingestion were not added to 

the noncancer hazard estimates from inhalation of indoor air during showering because the 

critical effect and target organs upon which the RfDs and RfCs were based were different for 

each solvent. 

Do not cite or quote. 3-57 
Review Draft Section 3.0 Analysis 

3.6.1.2 Key Assumptions 

This section presents key assumptions associated with the applied risk modeling 

approach. 

 Risk estimates presented in this document address hypothetical chronic exposures for 

these receptors and were designed to provide a realistic range of potential scenarios. Not 

all potential scenarios were evaluated; for example, infants (0- to 1-year-olds) were not 

evaluated. 

 The hierarchy of sources that was assumed and implemented for this analysis (U.S. EPA, 

2003f) uses the Integrated Risk Information System as a first-tier source, Provisional 

Peer-Reviewed Toxicity Values as a second-tier source, and other sources as third-tier 

sources. Confidence in human health benchmarks can be associated with the date the 

benchmarks were derived (or last reviewed). As additional toxicological data become 

available, the database becomes more complete. Methodologies evolve over time, with 

improvements in existing methods (e.g., EPA’s 1994 RfC methodology) and the 

development of new health benchmark practices (e.g., benchmark dose methodology). 

 Constituent-specific health benchmarks were used for all solvents. However, the RfD for 

m- and o-cresol was used as a surrogate for the cresol mixture assessed in the analysis; 

this is not thought to have introduced any significant uncertainty. 

 Although receptors are likely to be exposed to mixtures of solvents, noncancer hazard 

and cancer risk were assessed separately for each solvent. However, groundwater 

pathway exposures were added for each solvent for cancer endpoints and noncancer 

endpoints, respectively. For example, cancer risks across groundwater exposure pathways 

(i.e., ingestion of drinking water and inhalation of shower air) were combined for adults 

(children were not assumed to be exposed via the inhalation of shower air), irrespective 

of the type of cancer caused by each route of exposure, possibly introducing some 

uncertainty in the mass loading rates. 

 The potential for adverse health effects due to exposure to multiple solvents during the 

same exposure period was not evaluated; therefore, we did not consider the additivity, 

synergism, or antagonism of effects associated with potential, concurrent exposures to 

more than one solvent at a time. Noncancer hazard and cancer risk due to exposure to 

solvent “mixtures” may, therefore, have been under- or overestimated. However, for 

noncarcinogenic risks, risks for a specific solvent from ingestion of groundwater were 

added to the inhalation and dermal risks from showering/bathing for that solvent, if the 

data indicated that the target organ and effect are independent of the exposure route (as 

was the case for benzene, cresols, and methyl ethyl ketone). 

 Estimating dermal noncancer hazards and cancer risks with the unadjusted RfD and oral 

CSF benchmarks will in most cases result in an overestimation of human health impacts 

from dermal contact during a bathing or showering event. 

 We assumed that the shower or bath water contacted 100 percent of the body surface area 

in both the shower and bathing scenarios. Assuming 100 percent coverage will result in 

an overestimation of impacts. 

Do not cite or quote. 3-58 
Review Draft Section 3.0 Analysis 

3.6.2 Model Inputs 

The inputs to the human health effects modeling include estimates of toxicity (the human 

health benchmarks) and exposure doses or exposure concentrations. The exposure or media 

concentrations for ambient air and indoor air are discussed in Sections 3.2 and 3.4, respectively. 

Exposure doses are discussed in Section 3.5. The human health benchmarks used as input to the 

model are discussed below. 

Human health benchmarks for chronic exposures were used in this analysis to 

characterize the potential cancer risks and noncancer hazards associated with two management 

scenarios (lined and unlined landfills). Oral CSFs and inhalation URFs were used to produce risk 

estimates for carcinogens. RfDs and RfCs were used to estimate the noncancer hazards from oral 

and inhalation exposures, respectively. 

The CSF is an upper-bound estimate (approximating a 95 percent confidence limit) of the 

increased human cancer risk from a lifetime exposure to an agent. This estimate is usually 

expressed in units of proportion (of a population) affected per milligram of agent per kilogram 

body weight per day (mg/kg-d). The URF is the preferred cancer benchmark for inhalation 

exposures, where the air concentrations inhaled are used as the measure of dose. The unit risk is 

the upper-bound lifetime excess cancer risk estimated to result from continuous exposure to an 

agent at a concentration of 1 μg/m3 in air. That is, if the unit risk = 1.5 × 10-6 per μg/m3 , then 1.5 

excess cancer cases would be expected to develop per 1,000,000 people if exposed daily to a 

concentration of 1 μg of the chemical agent in 1 m3

 of air for a lifetime. Unlike RfDs and RfCs, 

CSFs and URFs do not represent “safe” exposure levels; rather, they relate levels of exposure to 

a probability of developing cancer. 

The RfD and RfC are the primary benchmarks used to evaluate noncarcinogenic hazards 

posed by environmental exposures to chemical constituents. The RfD and RfC are estimates 

(with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human 

population (including sensitive subgroups) that is likely to be without appreciable risk of 

deleterious noncancer effects during a lifetime. However, an average lifetime exposure above the 

RfD (or RfC) does not imply that an adverse health effect would necessarily occur (U.S. EPA, 

2007). 

Several sources of human health benchmarks are available. As per Office of Solid Waste 

and Emergency Response (OSWER) Directive 9285.7-53, EPA’s Integrated Risk Information 

System (IRIS) is the preferred source of human health benchmarks (U.S. EPA, 2003f). After 

IRIS, human health benchmarks were obtained from sources in the following order of 

preference: 

 Provisional Peer Reviewed Toxicity Values (PPRTVs) 

 Other sources include the following in order of preference (priority was given to those 

sources that were most current, those for which the basis was transparent and publicly 

available, and those that had been peer reviewed): 

− Agency for Toxic Substances and Disease Registry (ATSDR) 

− California Environmental Protection Agency (CalEPA) 

− Health Effects Assessment Summary Tables (HEAST). 

Integrated Risk Information System (IRIS). IRIS is EPA’s official electronic 

repository of chronic human health benchmarks and represents EPA-wide consensus on critical 

human health effects associated with exposure to chemical constituents released into the 

environment (U.S. EPA, 2007). Benchmarks in IRIS have been extensively reviewed, and each 

Do not cite or quote. 3-59 
Review Draft Section 3.0 Analysis 

file contains descriptive and quantitative information on potential health effects associated with 

the benchmark and other studies evaluated during its derivation. IRIS includes oral CSFs, 

inhalation URFs, oral RfDs, and inhalation RfCs. 

Provisional Peer-Reviewed Toxicity Values (PPRTV). EPA’s National Center for 

Environmental Assessment (NCEA)/Superfund Health Risk Technical Support Center derives 

provisional RfCs, RfDs, and CSFs for certain chemical constituents. These provisional values 

have been externally peer reviewed but have not undergone EPA’s formal review process for 

inclusion in IRIS. 

CalEPA Cancer Potency Factors and Reference Exposure Levels. CalEPA has 

developed cancer potency factors for chemical constituents regulated under California’s Air 

Toxics “Hot Spots” program (CalEPA, 2007). The cancer potency factors are analogous to 

EPA’s oral and inhalation CSFs. CalEPA has also developed chronic inhalation reference 

exposure levels (RELs), analogous to EPA’s RfCs (CalEPA, 2007). CalEPA used EPA’s 

inhalation dosimetry methodology (U.S. EPA, 1994a) in the derivation of chronic inhalation 

RELs. The cancer potency factors and inhalation RELs have undergone internal peer review by 

various California agencies and been the subject of public comment. 

ATSDR Minimal Risk Levels. The ATSDR minimal risk levels (MRLs) are substancespecific health guidance levels for noncarcinogenic endpoints (ATSDR, 2007). An MRL is an 

estimate of the daily human exposure to a hazardous substance that is likely to be without 

appreciable risk of adverse noncancer health effects over a specified duration of exposure. MRLs 

are based on noncancer health effects only and are not based on a consideration of cancer effects. 

MRLs are derived for acute, intermediate, and chronic exposure durations for oral and inhalation 

routes of exposure. Chronic inhalation and oral MRLs are similar to RfCs and RfDs, respectively 

(i.e., ATSDR uses the no observed adverse effect level [NOAEL]/uncertainty factor approach); 

however, MRLs are intended to serve as screening levels and are exposure duration specific. 

ATSDR also uses EPA’s inhalation dosimetry methodology (U.S. EPA, 1994a) in the derivation 

of inhalation MRLs. When based on the same critical study, the MRLs have similar toxicity 

endpoints but may apply different uncertainty factors than EPA’s RfDs and RfCs. MRLs are 

available on ATSDR’s Web site and are presented in detail in individual ATSDR toxicological 

profiles. MRLs have undergone internal peer review and been the subject of public comment. 

Health Effects Assessment Summary Tables (HEAST). HEAST is a listing of 

provisional noncarcinogenic and carcinogenic health benchmarks (i.e., RfDs, RfCs, CSFs, and 

URFs) derived by EPA (U.S. EPA, 1997f). HEAST benchmarks are gradually being replaced by 

PPRTVs or new IRIS benchmarks, and as such, HEAST has become a legacy database that is 

regarded as a third-tier source of toxicity values. 

The chronic human health benchmarks used in this analysis are summarized in 

Table 3-12. This table provides the constituent’s name, Chemical Abstract Service Registry 

Number (CASRN), RfD (in units of mg/kg-d), RfC (mg/m3

), oral CSF (CSFo) [(mg/kg-d)-1], 

inhalation URF [(μg/m3

)

-1], and reference (source) for each benchmark. The benchmark 

definitions and source descriptions are as described above. 

Note that for tetrachloroethylene, in addition to the CalEPA cancer values that were used 

in the analysis (CSForal of 5.4E-1 per mg/kg-day, URF of 5.9E-6 per μg/m3

), EPA’s Office of 

Pollution Prevention and Toxic Substances developed an inhalation cancer value that is based on 

the same study as the CalEPA values (U.S. EPA 1998). EPA’s Office of Research and 

Development released an update for tetrachloroethylene IRIS values, in external review draft 

Do not cite or quote. 3-60 
Review Draft Section 3.0 Analysis 

Do not cite or quote. 3-61 

form, for public and peer review (U.S. EPA 2008) that is undergoing review by a committee of 

the National Academy of Sciences, Board on Environmental Studies and Toxicology. 

3.6.3 Model Outputs 

The risk model generated a distribution of risk estimates for each scenario (solvent, liner 

type, contact medium, exposure route, receptor). Total risks from the groundwater pathway were 

calculated by summing the risks due to ingestion of drinking water, inhalation of shower 

emissions (for adults only), and dermal exposure during showering (adults) or bathing (children). 

Risks due to inhalation of ambient air were considered separately because these exposures 

typically do not occur in the same timeframe. As discussed in Section 5.0, the resulting risk 

estimates were used in determining the solvent-specific mass loading limits. These limits are 

defined as the mass loadings that result in cancer risks no greater than 1 in 100,000 (1E-5) and 

noncancer HQs no greater than 1 at the 90th percentile of the distribution of risks. 
Review Draft 

Do not cite or quote. 

Table 3-12. Chronic Health Benchmarks Used in the Analysis 

Constituent CASRN 

RfD 

(mg/kg-d) 

RfD 

Ref 

RfC 

(mg/m3) 

RfC 

Ref 

CSFo 

(per 

mg/kg-d) 

CSFo 

Ref 

Acetone 67-64-1 9.0E-01 IRIS 3.1E+01 ATSDR Benzene 71-43-2 4.0E-03 IRIS 3.0E-02 IRIS 5.5E-02 

1.5E-02a 

IRIS 

Butanol, n- (n-butyl alcohol) 71-36-3 1.0E-01 IRIS Chlorobenzene 108-90-7 2.0E-02 IRIS 5.0E-02 PPRTV Cresol mixtures 1319-77-3 5.0E-02 IRISb 6.0E-01 CalEPA Cyclohexanone 108-94-1 5.0E+00 IRIS Dichlorobenzene, 1,2- (o-) 95-50-1 9.0E-02 IRIS 2.0E-01 HEAST Ethoxyethanol, 2- (ethylene glycol monoethyl ether) 

110-80-5 4.0E-01 HEAST 2.0E-01 IRIS 

Ethyl acetate 141-78-6 9.0E-01 IRIS Ethylbenzene 100-41-4 1.0E-01 IRIS 1.0E+00 IRIS Isobutyl alcohol 78-83-1 3.0E-01 IRIS Methanol 67-56-1 5.0E-01 IRIS 4.0E+00 CalEPA Methyl ethyl ketone 78-93-3 6.0E-01 IRIS 5.0E+00 IRIS Methyl isobutyl ketone 108-10-1 8.0E-02 HEAST 3.0E+00 IRIS Methylene chloride (dichloromethane) 

75-09-2 6.0E-02 IRIS 4.0E-01 CalEPA 7.5E-03 IRIS 

Tetrachloroethylene (perchloroethylene) 

127-18-4 1.0E-02 IRIS 3.0E-01 ATSDR 5.4E-01 CalEPA 

Toluene 108-88-3 8.0E-02 IRIS 5.0E+00 IRIS Trichloroethane, 1,1,2- (vinyl trichloride) 

79-00-5 4.0E-03 IRIS 5.7E-02 IRIS 

Trichloroethylene 79-01-6 6.0E-01 CalEPA 1.3E-02 CalEPA Xylenes (total) 1330-20-7 2.0E-01 IRIS 1.0E-01 IRIS 

a High and low end of the range developed by EPA. b IRIS RfD for m- and o-cresol used as surrogate for all isomers. c In addition to the CalEPA values shown and used in this analysis, risks were also calculated using an inhalation unit risk estimate (URF of 7.1E-7 per μg

Office of Pollution Prevention and Toxic Substances (OPPTS) that is based on the same study (by the National Toxicology Program in 1986) as the CalE
Review Draft Section 4.0 Risk Characterization 

4.0 Risk Characterization 

The goal of this analysis was to derive solvent-specific mass loading rates, in kg solvent per 

year, that represent the maximum amount of solvent that can be managed annually in municipal 

landfills and be protective of public health. For the purpose of this analysis, EPA defines 

“protective” in terms of specific individual excess cancer risk probability to be no more than 1 

chance in 100,000, and for noncancer hazard, not exceeding a threshold dose or concentration. In the 

Guidance for Risk Characterization developed by the EPA Science Policy Council (U.S. EPA, 

1995b), EPA defined the high end of the risk distribution as being at or above the 90th percentile 

risk or hazard estimate generated during the Monte Carlo simulation. The high end of the risk 

distribution refers only to hypothetical individuals living near municipal landfills that may manage 

solvent-containing wipes and sludges. Thus, at a minimum, the mass loading rates were developed to 

ensure that solvents managed at 90 percent of the municipal landfills will not result in exposures for 

nearby residents above the risk criteria for cancer and noncancer endpoints. As discussed in 

Section 4.2, several key assumptions and policy decisions bias the risk analysis in the conservative 

direction, so that the mass loading rates will tend to be more protective. Moreover, exposure 

pathways may not be complete at many landfill sites because not all residents at all sites will rely on 

groundwater; lacking receptor wells, the groundwater pathway risk at these sites will be zero. 

Given that the purpose of the risk analysis is to estimate allowable mass loading rates that are 

protective of public health, this section first presents the results of the risk analysis. Specifically, we 

present the 50th and 90th percentile risk estimates based on a fixed input concentration using the 

methods and models described in Sections 2 and 3 for the Monte Carlo simulation. As explained in 

Section 2, these results are not estimates of the actual risks to human health for solvent-containing 

wipes and sludges as they are currently managed. The 50th and 90th percentile risk estimates were 

selected from the modeled theoretical risk distributions. These estimates were compared to the 

risk/hazard criteria to develop scaling factors used in the calculation of the risk-based mass loading 

rates (as described in Section 5). After presenting the risk results in Section 4.1, we then discuss the 

strengths and limitations of the risk analysis methodology in Section 4.2, emphasizing the key 

uncertainties inherent in the approach, models, and data used in this analysis. 

4.1 Human Health Risk 

This section presents the 50th and 90th percentile cancer risk and noncancer hazard estimates 

for the human exposure pathways evaluated in this analysis: (1) ambient air inhalation (2) 

groundwater-to-drinking-water ingestion, (3) groundwater-to-shower inhalation, and (4) dermal 

exposure to groundwater during showering or bathing. Because the risk results presented here 

represent the theoretical risk distributions based on the unitized mass loading rate of 1 kg/year, not 

actual risk estimates, they should not be regarded as representative of the actual risks associated with 

wipes and sludge management. Further, it should be noted that the theoretical risk distributions are 

derived irrespective of the source of the solvent; the risk estimates are applicable to any combination 

of solvent mass contributed by either or both disposed wipes and laundry sludges. Section 4.1.1

presents the ambient air pathway results and Section 4.1.2 presents the groundwater pathway results. 

Do not cite or quote. 4-1 
Review Draft Section 4.0 Risk Characterization 

4.1.1 Ambient Air 

Tables 4-1 and 4-2 present the 50th and 90th percentile ambient air inhalation risks for the 

adult and child receptors, respectively. In each table, the results are presented by liner type 

(composite liner or no liner) and endpoint (cancer or noncancer).17 

The adult cancer risks are slightly higher than the child risks in all cases, primarily because 

the distribution of exposure duration for adults results in longer predicted exposures for adults than 

for children. The child noncancer hazards (HQs) are slightly higher than the adult noncancer HQs 

because the noncancer risks are estimated by comparing the ambient air concentrations with the 

solvent-specific RfC (see Section 3.6), and those air concentrations are averaged over the receptor’s 

period of exposure, centered on the peak (or maximum) ambient air concentration (see Section 

3.2.3). In the case of noncancer endpoints, the shorter exposure durations for the child results in 

slightly higher average air concentrations and, consequently, higher risk estimates. 

Note that there is very little difference for the ambient air inhalation risks between the noliner and composite-liner risk estimates. This is to be expected, because the dominant release 

mechanism for solvent vapors is advection, which is at a maximum just after the waste is applied and 

is influenced only to a limited extent by the cover/liner type. The no-liner risks are slightly higher 

than the composite-liner risks probably because no-liner landfills are covered with only a soil layer 

after closure (vs. geomembrane and soil for the lined landfills), and therefore, the diffusive losses are 

likely to be somewhat higher for the no-liner landfills (soil cover) than for the composite-lined 

landfills (geomembrane and soil cover). This difference leads to greater diffusive losses to the 

atmosphere in the no-liner scenario after landfill closure. 

4.1.2 Groundwater 

Tables 4-3 and 4-4 present the 50th and 90th percentile risks, respectively, for groundwater 

exposures to adult receptors. Tables 4-5 and 4-6 present the 50th and 90th percentile risks, 

respectively, for the child receptor. In all four tables, results are grouped by liner type (composite 

liner or no liner) and endpoint (cancer or noncancer). Tables 4-3 and 4-4 present adult receptor 

results individually for each exposure route (i.e., drinking water ingestion, shower inhalation, dermal 

bathing/showering) and combined risks (i.e., adding the risks from the drinking water, shower 

inhalation and dermal pathways) for all carcinogens and a subset of noncarcinogens (i.e., benzene, 

cresol, and methyl ethyl ketone) that have RfD and RfCs with matching endpoints and no portal of 

entry effects. Tables 4-5 and 4-6 present child receptor results individually for each exposure route 

(i.e., drinking water ingestion and dermal bathing) and combined risks. In the case of the combined 

risks, the driving pathways are identified in parentheses in the tables. 

When reviewing Tables 4-3 through 4-6, note that the individual pathway results (i.e., 

drinking water ingestion, shower inhalation, and shower dermal for the adult and drinking water 

ingestion, shower inhalation, and bath dermal for the child) and combined results are each associated 

with a specific simulation run ID (see Appendix A for additional details). In a Monte Carlo 

simulation, different run IDs are associated with a different set of parameter values even though, at 

selected percentiles, these values are almost identical. For cancer and for noncancer endpoints that 

are the same for different routes of exposure, the implication of this approach is that the combined 

risk estimate for an integrated simulation uses a common set of input values and, as a result, the total 

risk due to concurrent exposure pathways may be slightly different than a risk estimate derived by 

adding the results for the individual pathways that were modeled independently. As one might 

 

17 Two separate estimates are presented for benzene cancer risks to reflect both the high and low ends of the range for 

inhalation and oral exposures. . 

Do not cite or quote. 4-2 
Review Draft Section 4.0 Risk Characterization 

expect, the driving pathway identified in the integrated simulation may or may not be the same as the 

driving pathway identified by comparing the drinking water ingestion result to the shower inhalation 

result. Thus, the driving pathway can “flip” depending on the specific set of parameter values. 

Table 4-1. Summary of 50th and 90th Percentile Solvent Wipes 

Human Risk at the Unitized Mass Loading Rate (1 kg / year) 

Adult Ambient Air Inhalation Pathway

50th Percentile Risk 90th Percentile Risk 

CAS Chemical NL Composite NL Composite 

Cancer 

79-00-5 1,1,2-Trichloroethane 3.0E-11 2.7E-11 2.7E-10 2.4E-10 

71-43-2 (H) Benzene(high)* 6.9E-11 7.3E-11 5.9E-10 6.1E-10 

71-43-2 (L) Benzene(low)* 1.9E-11 2.1E-11 1.7E-10 1.7E-10 

75-09-2 Methylene chloride 4.5E-12 4.6E-12 4.1E-11 4.1E-11 

127-18-4(H) Tetrachloroethylene(high)* 5.4E-11 5.7E-11 4.6E-10 4.8E-10 

127-18-4(L) Tetrachloroethylene(low)* 6.5E-12 6.9E-12 5.5E-11 5.7E-11 

79-01-6 Trichloroethylene 1.9E-11 2.0E-11 1.6E-10 1.6E-10 

Noncancer 

110-80-5 2-Ethoxyethanol 1.6E-10 1.5E-10 1.4E-09 1.3E-09 

67-64-1 Acetone 4.0E-11 3.0E-10 3.0E-10 2.2E-09 

71-43-2 Benzene 1.7E-06 1.9E-06 1.2E-05 1.2E-05 

108-90-7 Chlorobenzene 6.5E-07 6.5E-07 4.5E-06 4.4E-06 

1319-77-3 Cresols (total) 2.0E-10 1.8E-10 1.6E-09 1.4E-09 

95-50-1 Dichlorobenzene, 1,2 7.2E-08 6.7E-08 5.1E-07 4.8E-07 

100-41-4 Ethyl benzene 2.5E-08 2.4E-08 1.8E-07 1.7E-07 

67-56-1 Methanol 3.1E-10 2.7E-10 2.3E-09 2.0E-09 

78-93-3 Methyl ethyl ketone 1.0E-08 2.2E-09 7.1E-08 1.6E-08 

108-10-1 Methyl isobutyl ketone 2.4E-09 2.2E-09 1.8E-08 1.7E-08 

75-09-2 Methylene chloride 1.5E-07 1.5E-07 9.9E-07 9.9E-07 

127-18-4 Tetrachloroethylene 1.8E-07 1.9E-07 1.2E-06 1.3E-06 

108-88-3 Toluene 6.7E-09 6.6E-09 4.6E-08 4.5E-08 

79-01-6 Trichloroethylene 9.1E-08 9.8E-08 6.2E-07 6.5E-07 

1330-20-7 Xylene (mixed isomers) 1.2E-07 1.1E-07 8.5E-07 7.9E-07 

* high = high-end cancer benchmark range low = low-end cancer benchmark range 

Do not cite or quote. 4-3 
Review Draft Section 4.0 Risk Characterization 

Table 4-2. Summary of 50th and 90th Percentile Solvent Wipes 

Human Risk at the Unitized Mass Loading Rate (1 kg / year) 

Child Ambient Air Inhalation Pathway

50th Percentile Risk 90th Percentile Risk 

CAS Chemical NL Composite NL Composite 

Cancer 

79-00-5 1,1,2-Trichloroethane 1.2E-11 1.1E-11 1.2E-10 1.1E-10 

71-43-2 (H) Benzene(high)* 2.9E-11 3.1E-11 2.8E-10 2.8E-10 

71-43-2 (L) Benzene(low)* 8.3E-12 8.9E-12 7.8E-11 8.0E-11 

75-09-2 Methylene chloride 1.8E-12 1.9E-12 1.8E-11 1.8E-11 

127-18-4(H) Tetrachloroethylene(high)* 2.3E-11 2.5E-11 2.2E-10 2.2E-10 

127-18-4(L) Tetrachloroethylene(low)* 2.8E-12 3.0E-12 2.6E-11 2.7E-11 

79-01-6 Trichloroethylene 7.9E-12 8.5E-12 7.4E-11 7.6E-11 

Noncancer 

110-80-5 2-Ethoxyethanol 1.6E-10 1.5E-10 1.4E-09 1.3E-09 

67-64-1 Acetone 4.2E-11 3.1E-10 3.1E-10 2.3E-09 

71-43-2 Benzene 1.9E-06 2.1E-06 1.3E-05 1.3E-05 

108-90-7 Chlorobenzene 6.9E-07 7.0E-07 4.8E-06 4.8E-06 

1319-77-3 Cresols (total) 2.1E-10 1.9E-10 1.6E-09 1.5E-09 

95-50-1 Dichlorobenzene, 1,2 7.7E-08 7.2E-08 5.5E-07 5.1E-07 

100-41-4 Ethyl benzene 2.7E-08 2.6E-08 1.9E-07 1.8E-07 

67-56-1 Methanol 3.2E-10 2.9E-10 2.4E-09 2.1E-09 

78-93-3 Methyl ethyl ketone 1.1E-08 2.3E-09 7.6E-08 1.7E-08 

108-10-1 Methyl isobutyl ketone 2.5E-09 2.3E-09 1.9E-08 1.7E-08 

75-09-2 Methylene chloride 1.5E-07 1.6E-07 1.0E-06 1.0E-06 

127-18-4 Tetrachloroethylene 2.0E-07 2.1E-07 1.3E-06 1.4E-06 

108-88-3 Toluene 7.1E-09 7.0E-09 4.9E-08 4.8E-08 

79-01-6 Trichloroethylene 1.0E-07 1.1E-07 6.7E-07 7.0E-07 

1330-20-7 Xylene (mixed isomers) 1.3E-07 1.2E-07 9.0E-07 8.3E-07 

* high = high-end cancer benchmark range low = low-end cancer benchmark range 

Do not cite or quote. 4-4 
Review Draft Section 4.0 Risk Characterization 

Do not cite or quote. 4-5 
Review Draft Section 4.0 Risk Characterization 

Do not cite or quote. 4-6 
Review Draft Section 4.0 Risk Characterization 

Table 4-5. Summary of 50th Percentile Solvent Wipes 

Human Risk at the Unitized Mass Loading Rate (1 kg / year) 

Child Groundwater Exposures

Risk 

Drinking Water Dermal Bathing Combined DW/DM 

CAS Chemical NL Composite NL Composite NL Composite 

Cancer 

79-00-5 1,1,2-Trichloroethane 3.0E-09 PI 3.9E-10 PI 3.5E-09 

(DW) 

PI 

71-43-2 (H) Benzene(high)* 7.5E-09 3.9E-18 1.6E-09 7.2E-19 9.5E-09 

(DW) 

4.5E-18 

(DW) 

71-43-2 (L) Benzene(low)* 2.0E-09 1.1E-18 4.3E-10 2.0E-19 2.6E-09 

(DW) 

1.2E-18 

(DW) 

75-09-2 Methylene chloride 7.3E-10 2.4E-24 3.8E-11 1.6E-25 7.8E-10 

(DW) 

2.5E-24 

(DW) 

127-18-4(H) Tetrachloroethylene(high)* 5.8E-08 1.4E-17 1.6E-08 4.2E-18 7.9E-08 

(DW) 

1.9E-17 

(DW) 

127-18-4(L) Tetrachloroethylene(low)* 5.8E-08 1.4E-17 1.6E-08 4.2E-18 7.9E-08 

(DW) 

1.9E-17 

(DW) 

79-01-6 Trichloroethylene 1.5E-09 3.0E-19 5.6E-10 1.2E-19 2.2E-09 

(DW) 

4.3E-19 

(DW) 

Noncancer 

79-00-5 1,1,2-Trichloroethane 2.1E-04 PI 2.8E-05 PI NA NA 

110-80-5 2-Ethoxyethanol 4.9E-07 1.2E-16 3.3E-09 7.2E-19 NA NA 

67-64-1 Acetone 1.2E-06 9.2E-18 8.0E-09 5.8E-20 NA NA 

71-43-2 Benzene 5.5E-04 2.9E-13 1.2E-04 6.8E-14 6.8E-04 

(DW) 

3.6E-13 

(DW) 

71-36-3 Butanol 1.7E-06 4.1E-13 5.4E-08 1.2E-14 NA NA 

108-90-7 Chlorobenzene 7.2E-05 1.8E-15 3.9E-05 9.4E-16 NA NA 

1319-77-3 Cresols (total) 8.6E-06 3.3E-15 1.2E-06 5.0E-16 1.0E-05 

(DW) 

3.8E-15 

(DW) 

108-94-1 Cyclohexanone 2.7E-07 1.8E-14 7.0E-09 4.2E-16 NA NA 

95-50-1 Dichlorobenzene, 1,2 7.7E-06 9.6E-19 7.8E-06 1.0E-18 NA NA 

141-78-6 Ethyl acetate 6.7E-10 PI 1.4E-11 PI NA NA 

100-41-4 Ethyl benzene 8.7E-06 1.8E-17 7.6E-06 1.5E-17 NA NA 

78-83-1 Isobutanol 4.2E-07 1.7E-14 1.1E-08 4.4E-16 NA NA 

67-56-1 Methanol 3.7E-06 8.8E-17 1.4E-08 3.5E-19 NA NA 

78-93-3 Methyl ethyl ketone 1.2E-06 1.5E-16 1.7E-08 1.8E-18 1.3E-06 

(DW) 

1.5E-16 

(DW) 

108-10-1 Methyl isobutyl ketone 3.3E-06 9.5E-16 1.5E-07 4.0E-17 NA NA 

75-09-2 Methylene chloride 2.5E-05 7.2E-20 1.4E-06 4.2E-21 NA NA 

127-18-4 Tetrachloroethylene 1.8E-04 3.7E-14 5.1E-05 1.1E-14 NA NA 

108-88-3 Toluene 2.0E-05 1.4E-15 1.0E-05 6.9E-16 NA NA 

1330-20-7 Xylene (mixed isomers) 2.8E-06 8.2E-19 2.5E-06 6.7E-19 NA NA 

(DM) - Driving pathway is dermal bathing * high = high-end cancer benchmark range(DW) - Driving pathway is drinking water low = lowend cancer benchmark range NA - Not applicable NHB - No health benchmark PI - Pathway incomplete because solvent does not reach

receptor well during simulation. 

Do not cite or quote. 4-7 
Review Draft Section 4.0 Risk Characterization 

(DM) - Driving pathway is dermal bathing * high = high-end cancer benchmark range(DW) - Driving pathway is 

drinking water low = low-end cancer benchmark range NA - Not applicable NHB - No health benchmark PI - Pathway 

incomplete because solvent does not reach receptor well during simulation.

Do not cite or quote. 4-8 
Review Draft Section 4.0 Risk Characterization 

Do not cite or quote. 4-9 

 The groundwater pathways for both the adult and child included drinking water ingestion 

and dermal exposure via bathing (i.e., showering for the adult and bath for the child). Comparing 

adult to child results for the drinking waster ingestion pathway, we see that the noncancer risks are 

higher for the child receptor, but the cancer risks are typically higher for the adult receptor. The adult 

cancer risk estimates are slightly higher because of the difference in the adult distribution for 

exposure duration as compared to the child. The child noncancer risks are higher because the ratio of 

consumption rate to body weight tends to be higher for younger children than for adults (see 

Sections 3.5 and 3.6 for drinking water dose and hazard quotient calculations). Comparing adult to 

child results for dermal exposure, the dermal noncancer risks are slightly higher for the child 

receptor, however the cancer risks for both receptors are very similar. The drinking water risks are 

higher than the dermal risk for all receptor/chemical/ endpoint combinations except for the child, 

noncancer hazard for 1,2-dichlorobenzene. In this case, the drinking water and dermal noncancer 

hazards are essentially the same. 

The shower inhalation results for adults are presented with drinking water ingestion and 

dermal results in Tables 4-3 and 4-4. As noted above, the adult cancer and noncancer hazards for 

drinking water ingestion are always higher than the dermal estimates. The noncancer results for 

drinking water ingestion for adults were higher than for shower inhalation for all solvents except 

benzene, chlorobenzene, 1,2-dichlorobenzene, ethyl benzene, methylene chloride, and xylene. The 

relatively high Henry’s law constants for these solvents indicate that they are among the most 

volatile constituents, and therefore, the vapor phase concentration is likely to be much higher during 

showering. There is one exception to this trend: the predicted drinking water hazard for 

tetrachloroethylene is higher than for showering despite this solvent’s relatively high Henry’s law 

constant. This is likely because the toxicity of tetrachloroethylene is relatively high for the ingestion 

pathway as compared to its toxicity for the inhalation pathway (see Section 3.6.2). Similarly, the 

shower inhalation cancer risks are higher than the drinking water ingestion risks for all chemicals 

except tetrachloroethylene. 

As expected, the predicted groundwater pathway results for the composite-lined landfills are 

lower than the results for unlined landfills. In Table 4-3, note that the 50th percentile results for 

1,1,2-tricholoethane and ethyl acetate for the composite liner scenario show that these two solvents 

did not reach the receptor well during the 10,000 year simulation run using EPACMTP. Thus, for the 

groundwater results, it is also important to consider the travel time, that is, the number of years that 

the model predicts it will take the solvent to reach the receptor well after it leaches out from the 

bottom of the landfill. 

 Estimates of peak concentration arrival times were generated for each Monte Carlo 

simulation of groundwater pathway scenarios (e.g., solvent, liner type), and can be examined as a 

cumulative distribution of observations. Table 4-7 provides information on the elapsed travel time 

(in years) associated with peak exposure concentration at the receptor well. The elapsed time is 

from the onset of leaching into the subsurface up until the time the peak concentration was predicted 

to arrive at the well, for all groundwater pathway Monte Carlo realizations by solvent and liner 

scenario. The meaning of any individual value shown in the table, with units of years, corresponds to 

a percentile can be interpreted as the maximum number of years it takes X percent of the peak 

observed concentrations to arrive at the receptor well for a given solvent/liner type set of Monte 

Carlo modeling realizations. The table indicates that, according to the modeling assumptions, the 

presence of a composite liner can significantly increase the predicted time required for a given 

solvent to reach the receptor well. However, because composite liners have only been in widespread 

use for a few decades at most, these modeling results must be interpreted cautiously. 
Review Draft Section 

Table 4-7. Travel Time Associated with the Peak Exposure Concentration at the Receptor 

Percentiles of Time to the Arrival of the Peak Observed Concentr

Reaching the Receptor Well (years)a

Constituent Name CAS No. Liner 10% 20% 30% 40% 50% 60% 70% b 

NL 15 31 43 54 65 79 

Acetone 67641 

GM 0 0 0 0 133 361 

NL 31 44 54 65 78 95 

Benzene 71432 

GM 0 0 0 0 242 524 1

NL 36 48 58 68 81 97 

Butanol, n- (n-butyl alcohol) 71363 GM 0 0 0 248 615 1189 2

NL 41 58 75 94 121 161 

Chlorobenzene 108907 

GM 0 0 0 0 327 852 1

NL 41 57 73 91 113 144 

Cresol mixtures (Tricresol) 1319773 GM 0 0 0 0 315 649 1

NL 40 54 66 80 97 119 

Cyclohexanone 108941 GM 0 0 0 292 710 1402 2

NL 57 87 122 168 233 329 

Dichlorobenzene, 1,2- (o-) 95501 GM 0 0 0 0 412 1514 3Ethoxyethanol, 2- NL 33 44 53 63 74 88 (ethylene glycol monoethyl ether) 110805 GM 0 0 0 0 174 401 

NL 0 0 33 44 53 62 

Ethyl acetate 141786 GM 0 0 0 0 0 0 

NL 53 79 107 143 197 278 

Ethylbenzene 100414 GM 0 0 0 0 434 1413 3

NL 13 25 41 53 65 80 

Isobutyl alcohol 78831 GM 0 0 0 195 511 1077 2

NL 35 48 60 71 84 102 

Methanol 67561 

GM 0 0 0 0 203 390 

NL 14 29 42 53 65 79 

Methyl ethyl ketone 78933 GM 0 0 0 0 157 409 

Do not cite or quote. 
aft Section 

Do not cite or quote. 

Table 4-7. (continued) 

Percentiles of Time to the Arrival of the Peak Observed Concentr

Reaching the Receptor Well (years)a

Constituent Name CAS No. Linerb 10% 20% 30% 40% 50% 60% 70% 

NL 15 31 44 55 67 81 

Methyl isobutyl ketone 108101 GM 0 0 0 0 176 443 

NL 12 27 41 52 64 77 

Methylene chloride (dichloromethane) 75092 GM 0 0 0 0 78 279 

NL 34 47 59 73 89 112 

Tetrachloroethylene (perchloroethylene) 127184 GM 0 0 0 0 273 637 1

NL 39 55 69 87 108 140 

Toluene 108883 

GM 0 0 0 0 331 769 1

Trichloroethane, 1,1,2- NL 30 48 59 72 86 104 (vinyl trichloride) 79005 GM 0 0 0 0 1 296 

NL 32 46 56 69 84 105 

Trichloroethylene 79016 GM 0 0 0 0 250 583 1

NL 59 90 127 172 239 333 

Xylenes (total) 1330207 GM 0 0 0 0 433 1548 3a A value of “0” indicates that no mass reached the water table, and a value of “1” indicates that mass reached the water table but not the receptor web NL = No Liner, GM = Geomembrane Composite Liner 

Review Dr
Review Draft Section 4.0 Risk Characterization 

4.2 Uncertainty Characterization 

The risk estimates in this section provided a great deal of information about the relative 

nature of exposures and potential risks. For example, the central tendency results (50th percentile) 

suggested that the groundwater exposure pathways are the driving pathways (i.e., highest risk) for 

the no-liner scenario, whereas the ambient air pathway is the predominant driving pathway for the 

composite-liner scenario. This is consistent with our expectations because the composite-liner should 

prevent or limit leaching to groundwater, reducing the potential for exposure via this pathway. 

Interestingly, we see that this trend did not hold true for the 90th percentile results. At the high end 

of the risk distribution, the groundwater risk estimates for the composite-liner scenario increased 

dramatically, and it was apparent that ambient air inhalation was not always the driving pathway. 

Again, this is consistent with our expectations in a Monte Carlo simulation: the groundwater 

pathway risk estimates at the 90th percentile represent lower probability combinations of landfill 

characteristics (e.g., liner leakage) and environmental conditions (e.g., high infiltration rates) that 

favor leaching and groundwater transport. Importantly, the relative difference between the 50th and 

90th percentile values for the groundwater pathways is greater than it is for the ambient air 

inhalation pathway, suggesting that the variability in the groundwater pathway simulations is 

significantly higher than the variability associated with the ambient air pathway simulations. 

Although the risk estimates provide substantial 

insight into the potential for exposure and adverse health 

effects, there are a number of uncertainties inherent in 

predictive risk assessment modeling. As suggested in 

Section 2.2.2, this uncertainty characterization will 

primarily address Type A uncertainty, the uncertainty 

associated with variability. Variability represents true 

heterogeneity in characteristics, such as the range of 

body weights across an adult population or the 

hydrogeologic properties characteristic of different 

regions across the country. Although variability may be 

known with great certainty (e.g., the age distribution of a 

population may be known and represented by the mean 

age and its standard deviation), it cannot be eliminated and should be treated explicitly in the 

assessment. Uncertainty is a description of the imperfection in knowledge of the true value of a 

particular parameter or ability of a model to accurately predict the behavior of a particular solvent. In 

contrast to variability, uncertainty can be reduced through additional information data gathering, data 

analysis, or model research (i.e., better data, better models). 

Variability arises from true heterogeneity in 

characteristics, such as body weight 

differences within a population or differences 

in contaminant levels in the environment. 

Uncertainty represents a lack of knowledge 

about factors such as the actual rate of 

biodegradation in the environment, which 

may be reduced with additional research to 

improve data or models. 

In this section, we discuss three major sources of uncertainty: (1) the uncertainty associated 

with the conceptual approach developed for the risk analysis (e.g., how does the structure of the 

assessment influence the risk estimates?), (2) the uncertainty associated with the models used (e.g., 

how well does a model represent reality?), and (3) the uncertainty associated with the distributions 

and values assigned to input parameters, particularly those parameters to which the model is most 

sensitive (e.g., what are the true values of each parameter?). We present qualitative descriptions of 

uncertainty, indicating whether or not a particular assumption or key data input tends to be 

conservative and roughly what level of bias is likely to be introduced (e.g., more than an order of 

magnitude). As appropriate, we describe the strengths and limitations of various modeling 

Do not cite or quote. 4-12 
Review Draft Section 4.0 Risk Characterization 

components, noting where it was necessary to simplify a mathematical construct or use a fixed value 

for an input parameter. And lastly, we offer several “side-analyses” that quantitatively address 

potential uncertainties associated with the analysis; in general, these side-analyses were developed to 

address peer-review comments on a previous draft of the technical support document. 

4.2.1 Conceptual Approach 

As discussed in Section 2, the objective of this analysis was to support a national analysis of 

solvent-containing wipes and laundry sludge that may be managed in landfills, and to establish 

allowable mass loading rates for each solvent that would be protective of public health. To achieve 

this objective, we developed a conceptual approach that frames the risk analysis in terms of (1) the 

scenarios that were included in the model simulations (i.e., what we modeled) and (2) the Monte 

Carlo framework used to generate the distributions of risk estimates (i.e., how we modeled it). 

Below, we discuss these two aspects of the conceptual approach, pointing out several key 

uncertainties/limitations. 

4.2.1.1 Modeling Scenarios 

The modeling scenarios were constructed to represent the potential high-end risks to human 

health that could be attributed to the disposal of solvent-containing wipes and laundry sludge in 

municipal landfills. The modeling scenarios describe the chemical constituents of interest (specific 

solvents), the source of contaminant release (municipal landfills with no liner and with a composite 

liner), the exposure pathways (e.g., transport in air from the source to potential inhalation receptors), 

and the receptors (child and adult receptors associated with particular exposure pathways). However, 

this is not an exhaustive list of modeling scenarios, and using risk estimates from this subset raises 

several questions regarding the representativeness of the scenarios with respect to potential risks. 

That is, are these scenarios likely to capture the high-end risks to human health associated with 

landfill management of solvent-containing wipes and laundry sludge? We identified the following 

issues as uncertainties/limitations inherent in the modeling scenarios chosen for this analysis. 

 The analysis did not consider lateral migration of landfill gas from landfills in the subsurface 

and potential collection and exposure in buildings. Although this transport mechanism has 

been documented in particular cases, it is not believed to be a dominant transport pathway for 

most landfills. If lateral migration of landfill gas results in vapor intrusion at concentrations 

above the levels predicted for the shower scenario, risk estimates for the current scenarios 

may not be protective for this pathway. 

 Potential mass release from landfills due to episodic precipitation events was not modeled; it 

was implicitly assumed that existing landfill controls (e.g., berms) would prevent any rain 

from “washing through” the landfill and possibly delivering solvents to nearby surface 

waters. This is an issue of temporal scale, because short-term, acute exposures were not 

evaluated either for human health or the environment. Mass loading rates developed for an 

episodic event scenario could be significantly different than the mass loading rates based on 

chronic risk; the relative values would depend on the endpoint considered (e.g., acute 

toxicity), the location of the landfill (e.g., proximity to residents), and other scenario 

characteristics. Thus, the mass loading rates for long-term, low-level solvent releases could 

be over- or underprotective relative to catastrophic release scenarios. 

Do not cite or quote. 4-13 
Review Draft Section 4.0 Risk Characterization 

 Potential risks to aquatic life associated with surface water contamination due to groundwater 

interactions were not evaluated. Given the chemical and physical properties of the 

constituents, we assumed that natural dilution and attenuation mechanisms would result in 

negligible risks to aquatic biota. National Recommended Water Quality Criteria were not 

available for any of the solvent constituents; however, a quick comparison between Tier II 

water quality criteria (Suter and Tsao, 1996) and the Maximum Contaminant Levels (U.S. 

EPA, 2006d) for the solvents evaluated suggests that risks to aquatic life will be below levels 

of concern if human health is adequately protected. Lack of a quantitative ecological risk 

screening is a limitation of our conceptual approach. 

4.2.1.2 Monte Carlo Approach 

To address the uncertainty associated with variability, we varied parameter values from 

realization to realization by randomly sampling from parameter-specific distributions.18 The 

sampling process accounted for correlated data (e.g., landfill characteristics), and importantly, 

maintained the spatial relationships among the landfill location, climate region, and groundwater 

region. We developed information on the form and characteristics of the data distributions for input 

parameters required by the various models. However, implementing the Monte Carlo framework 

also required us to determine how best to use available information as well as how many realizations 

were sufficient. The implications of several key decisions are discussed below. 

 We selected the locations for MSWLFs based on a distribution of 66 offsite Industrial D 

landfill locations, under the assumption that a national distribution of municipal landfills can 

be reasonably represented by a distribution of offsite industrial landfills. Municipal landfill 

locations are likely correlated with population and industrial centers in a pattern similar to 

that of industrial landfills (e.g., a higher density in areas with greater population density). The 

lack of an alternative data source to represent the national distribution of MSWLF locations 

is a potentially significant source of uncertainty; the location determines a number of 

environmental setting properties (e.g., depth to water table), which in turn affect the risk 

estimation. This unquantified source of uncertainty could result in either an over- or 

underprediction of risk, depending on the representativeness of the Industrial D data set. 

 Several critical landfill characteristics (area, volume, operating life) were based on 

parametric statistics from the EPA-WBJ database19 and not determined through sampling of 

actual landfills in the database. Given the proprietary nature of the EPA-WBJ database, we 

could not sample landfills directly. This would have been the preferred method, because 

sampling the EPA-WBJ landfill database would have created a data set of actual landfills. 

After several failed attempts to produce a correlated data set of landfill characteristics from 

the parametric statistics, we created a representative data set that adhered to a set of basic 

engineering principles and reasonable minima and maxima for each landfill characteristic 

(e.g., landfill depth could not be 1 cm; active life could not be 2,000 years). A comparison of 

the resulting data set to available information on municipal landfills suggests that the data set 

is, in fact, representative (see Table 3-2). 

 

18 For some parameters such as those that describe the hydrogeological setting, the distributions were allowed to co-vary as 

determined in readily available studies. 

19 As discussed in Section 3.1, the database obtained commercially by EPA from the Waste Business Journal (WBJ) and was analyzed 

and considered a representative data source for municipal landfill geometry and operating life characteristics. 

Do not cite or quote. 4-14 
Review Draft Section 4.0 Risk Characterization 

 The choice of 10,000 iterations in this analysis was based on our historical knowledge of 

conducting Monte Carlo simulations using these models for EPA risk analyses. Specifically, 

based on more than a decade of modeling experience, we concluded that 10,000 iterations are 

adequate to ensure stability of the results at the 90th percentile and other percentiles of 

interest. In response to peer-review comments, we evaluated this assumption by performing a 

stability assessment for two chemicals with very different chemical-physical properties, 2-

ethoxyethanol (least volatile) and tetrachloroethylene (most volatile). In essence, we sampled 

the results from 10,000 Monte Carlo simulations for several modeling scenarios (e.g., unlined 

landfill, adult shower inhalation) to create simulations of 4,000; 6,000; and 8,000 

realizations. The results shown in Figures 4-1 and 4-2 demonstrate that using10,000 

iterations adequately ensures the stability of the results at the percentiles of interest (50th and 

90th). At the 99th percentile, the results begin to deviate, indicating that more realizations are 

required to ensure stability at the extreme tail of the distribution. 

Figure 4-1. Comparison of different numbers of iterations for 2-ethoxyethanol (least volatile). 

2-Ethoxyethanol

Ambient Air/Adult

0.00E+00

1.00E-09

2.00E-09

3.00E-09

4.00E-09

5.00E-09

6.00E-09

7.00E-09

8.00E-09

50 60 70 80 90 100

Percentile

Non-cancer risk

4000

6000

8000

10000

2-Ethoxyethanol

Groundwater/Adult/Shower inhalation

0.00E+00

5.00E-07

1.00E-06

1.50E-06

2.00E-06

50 60 70 80 90 100

Percentile

Non-cancer risk

4000

6000

8000

10000

(a) (b) 

2-Ethoxyethanol

Groundwater/Adult/Drinking water

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

50 60 70 80 90 100

Percentile

Non-cancer risk

4000

6000

8000

10000

[combined not calculated for noncarcinogen] 

(c) 

Do not cite or quote. 4-15 
Review Draft Section 4.0 Risk Characterization 

Tetrachloroethylene

Ambient Air/Adult

0.00E+00

5.00E-10

1.00E-09

1.50E-09

2.00E-09

2.50E-09

50 60 70 80 90 100

Percentile

Cancer risk

4000

6000

8000

10000

Tetrachloroethylene

Groundwater/Adult/Shower inhalation

0.00E+00

2.00E-07

4.00E-07

6.00E-07

8.00E-07

1.00E-06

1.20E-06

50 60 70 80 90 100

Percentile

Cancer risk

4000

6000

8000

10000

(a) (b) 

Tetrachloroethylene

Groundwater/Adult/Combined

0.00E+00

1.00E-06

2.00E-06

3.00E-06

4.00E-06

5.00E-06

6.00E-06

50 60 70 80 90 100

Percentile

Cancer risk

4000

6000

8000

10000

Tetrachloroethylene

Groundwater/Adult/Drinking water

0.00E+00

5.00E-07

1.00E-06

1.50E-06

2.00E-06

2.50E-06

3.00E-06

3.50E-06

4.00E-06

4.50E-06

50 60 70 80 90 100

Percentile

Cancer risk

4000

6000

8000

10000

(c) (d) 

Figure 4-2. Comparison of different numbers of iterations for tetrachloroethylene (most 

volatile). 

4.2.2 Model Uncertainty 

Models used in this analysis were selected based on science, policy, and professional 

judgment of the technical staff at EPA and RTI. In general, we chose the models based on previous 

use in similar regulatory decision making, together with the level of scrutiny that they had previously 

received, including independent peer review by subject matter experts, the EPA Science Advisory 

Board, and the Office of Management and Budget. For example, the groundwater model 

(EPACMTP) has been used by the Office of Resource Conservation and Recovery (formerly the 

Office of Solid Waste) for over a decade in performing chemical risk assessments, and has been 

extensively peer reviewed and evaluated by EPA’s Science Advisory Board. In addition, we 

developed technical criteria with which to evaluate our modeling options. For instance, none of the 

landfill models that we evaluated were capable of simulating the effects of landfill gas on transport 

and, given the properties of the chemical constituents, we determined that the landfill model must be 

able to simulate this process. Thus, we developed the LFCR specifically for this analysis and 

engaged several subject matter experts as advisors during the model development and 

parameterization. 

Regardless of the level of peer review and validation efforts, all of the models used in this 

risk analysis are subject to some level of model uncertainty because: (1) mathematical expressions 

used to represent complex fate and transport processes are simplifications of reality and (b) even 

simple models like the exposure model implicitly assume that individual behavior will result in a 

non-zero exposure if the contact medium is contaminated (e.g., receptor wells are assumed to be 

Do not cite or quote. 4-16 
Review Draft Section 4.0 Risk Characterization 

present at every landfill site and accessible to receptors). Naturally, the predictive ability of the 

models depends both on how well we understand the key processes, our ability to develop a 

reasonable mathematical construct of these processes, and the availability of data on input 

parameters. In most cases, we simply do not have sufficient information to validate the models with 

field studies that span the full range of environmental conditions; as a result, we accept the fact that 

the use of computer models—regardless of how thoroughly they are tested—is associated with some 

level of uncertainty. Although we employ a variety of methods ranging from mass balance checks to 

literature reviews, it is not possible to further reduce the model uncertainty without rigorous field 

testing and sampling. For example, fluxes from the source model (air and leachate emissions) were 

averaged over the total surface area of the landfill, even though the landfill’s area increases over 

time until all landfill cells are covered with waste. This averaging provides compatibility with the air 

and groundwater transport models, which do not allow for an increasing source area over time. 

Recognizing that this is a simplification needed to allow the models to “communicate,” we 

implemented several checks to ensure that the overall solvent mass balance is maintained. This 

represents a modeling uncertainty that could be addressed by revising the downstream models (e.g., 

EPACMTP); an option that would require considerable time and resources. 

In the remainder of our discussion on model uncertainty, we focus on several key issues—

several raised by peer reviewers—that should be acknowledged when interpreting the results of the 

risk analysis. 

4.2.2.1 Landfill Source Model 

The LFCR model is based on a relatively simple conceptualization of landfill processes. For 

this national-scale risk analysis, site-specific data were not available; consequently, we carefully 

considered the practical implications of using site-based (e.g., regional) and representative (e.g., 

landfill characteristics) data to run the simulations. A more detailed, mechanistic model would have 

been difficult (or impossible) to parameterize and probably not feasible given the computational 

demands for this national analysis. Although the model strikes a reasonable balance among model 

complexity, precision, data availability, and implementability, one of the peer reviewers pointed out 

that ignoring leachate handling could represent a significant deficiency in our source modeling 

approach. 

In modern landfills, recovered leachate is typically managed in leachate collection systems. 

Some landfills send the recovered leachate to a treatment plant (municipal or other) while, in other 

cases, leachate is stored and often degraded in aerated surface impoundments. Aerobic degradation 

could release volatile chemicals to the air, a scenario that is not currently represented as part of the 

source modeling. Including this mass transfer would have required the inclusion of a surface 

impoundment source model, which was considered to be outside of the scope of this analysis. 

However, to address the peer reviewer’s concerns, we evaluated the potential significance of this 

pathway20 by considering how the air pathway risks and corresponding mass loading rates would 

change if all leachate mass volatilized (a highly conservative assumption). 

Table 4-8 presents the minimum mass loading rates for the air and groundwater pathways 

that meet the risk criteria; these are the mass loading rates associated with the greatest estimated risk 

for the air and groundwater pathways, respectively, at the 90th percentile of the risk distribution. The 

table also includes an estimate of the mass loading rate associated with the air pathway if all leachate 

 

20 Note that the leachate handling issue is of concern only for the composite liner scenario, because leachate is not collected in an 

unlined landfill. 

Do not cite or quote. 4-17 
Review Draft Section 4.0 Risk Characterization 

Do not cite or quote. 4-18 

mass volatilized. This estimate is based on the Mass Loss Ratio, also shown in the table, which is the 

ratio between the average cumulative total mass lost to the atmosphere plus 100 percent of the 

average cumulative total leachate mass loss divided by the average cumulative total mass lost to the 

atmosphere (i.e., excluding the leachate mass). Accordingly, a Mass Loss Ratio close to 1 indicates 

that a much greater portion of the mass left the landfill through the air pathway versus the leachate 

pathway. The Mass Loss Ratio increases as the proportion of mass lost via the leachate pathway 

increases. Because mass is conserved and the mass loading rate is linear with respect to model 

outputs, we can use the Mass Loss Ratio to estimate an allowable mass loading rate at the 90th 

percentile as though all of the leachate mass volatilized into the air. Thus, the adjusted mass loading 

rates in the table are calculated simply by dividing the minimum air pathway mass loading rate by 

the Mass Loss Ratio. 

The smallest mass loading rate for a chemical among all pathways is the mass loading limit 

(MLL), which is based on the greatest estimated risk (see Section 5). In all cases except methyl ethyl 

ketone, the groundwater pathway is associated with the MLL. If 100% of the leachate mass is added 

to the air pathway, the MLL would change for only four chemicals (2-ethoxyethanol, acetone, 

methyl ethyl ketone, methyl isobutyl ketone). For all but one of these chemicals (2-ethoxyethanol), 

the adjusted MLL exceeds the solubility limit (see Section 5). If a mass loading rate exceeds the 

solubility limit, the solubility limit is used. Therefore, the final MLL would only change for one 

chemical (2-ethoxyethanol) if all leachate mass were added to the air pathway. However, the 

assumption that 100 percent of the solvent volatilizes to air is highly conservative and, therefore, the 

result is really a bounding estimate of the change in mass loading rates. Because the volatility of 2-

ethoxyethanol is relatively low (the least volatile chemical considered), we would expect 

significantly less than 100 percent of the solvent to volatilize from an impoundment. Moreover, 

biodegradation accounts for greater than 97 percent of the mass loss from the landfill for 2-

ethoxyethanol (see Table 3-5). Based on the low volatility and high biodegradation potential for this 

solvents, it is highly unlikely that including air emissions associated with leachate handling would 

result in a significant change (if any) to the MLL. 
Review Draft Section 

Do not cite or quote. 

Table 4-8. Minimum Mass Loading Rates for Air Pathway Adjusted to Include Leachate Mass (Compos

Minimum Mass Loading Rates 

(kg/yr) 

Adjusted M

Chemicala HLCb (atm-m3/mol) Kocc (L/kg) 

Air Pathway Groundwater Pathway 

Average Mass 

Loss Ratiod

Air Pathway 

2-Ethoxyethanol 1.2E-07 1.6E+01 7.6E+08 8.1E+07 5.9E+01 Cresols (total) 9.5E-07 3.5E+01 6.7E+08e 1.4E+06 1.8E+01 Methanol 4.6E-06 9.0E+00 4.7E+08 1.2E+07 6.8E+00 Acetone 3.9E-05 1.0E+00 4.4E+08e 3.6E+08e 1.6E+00 Methyl ethyl ketone 5.6E-05 9.3E-01 6.0E+07e 1.9E+08e 1.4E+00 Methyl isobutyl ketone 1.4E-04 7.4E+00 5.8E+07e 5.6E+07e 1.2E+00 1,1,2-Trichloroethane 8.2E-04 1.5E+02 4.1E+04f 1.1E+04f 1.0E+00 Dichlorobenzene, 1,2- 1.9E-03 2.8E+02 2.0E+06 1.4E+06 1.0E+00 Methylene chloride 2.2E-03 8.5E+00 2.4E+05f 1.4E+06f 1.0E+00 Chlorobenzene 3.7E-03 1.6E+02 2.1E+05 2.6E+04 1.0E+00 Benzene (high)g 5.5E-03 6.3E+01 1.6E+04f 3.6E+03f 1.0E+00 Benzene (low) g 5.5E-03 6.3E+01 5.9E+04f 1.3E+04f 1.0E+00 Toluene 6.6E-03 2.7E+02 2.1E+07e 6.1E+05 1.0E+00 Xylene (mixed isomers) 6.7E-03 1.2E+03 1.2E+06 5.4E+05 1.0E+00 Ethyl benzene 7.9E-03 5.2E+02 5.5E+06 2.6E+06 1.0E+00 Trichloroethylene 1.0E-02 1.0E+02 6.1E+04f 1.4E+04f 1.0E+00 Tetrachloroethylene 1.8E-02 2.2E+02 2.1E+04f 9.5E+02f 1.0E+00 

a Chemicals are sorted from least to most volatile. Butanol, cyclohexanone, ethyl acetate, and isobutanol are not shown because they have no inhalati

there are no calculated air pathway risks. 

b HLC is the Henry’s Law Constant (normalized at 25o C), a measure of the solubility of gases. c Koc is the organic carbon partition coefficient, a measure of the partitioning between water and solid phases. d The Average Mass Loss Ratio is calculated from average cumulative total mass losses as follows: (air pathway + leachate pathway)/air pathway. e The mass loading rate exceeds the solubility limit; however, for purposes of the present calculation, raw mass loading rates were used rather than su

based values (see Section 5 for discussion of solubility based limits). 

f Based on a cancer risk of 10-5. The Mass Loss Ratio is very close to 1 for all carcinogens considered here. Therefore, the minimum mass loading rat

unchanged if all leachate mass were added to the air pathway even if an alternative cancer risk threshold were selected (10-4 or 10-6). 

g high = high-end benzene cancer health benchmark and low = low-end benzene cancer health benchmark. 
Review Draft Section 4.0 Risk Characterization 

4.2.2.2 Air Dispersion Model 

The Human Health Risk Protocol for Hazardous Waste Combustion Facilities (U.S. EPA, 

2005c) provides the following discussion on some of the uncertainty involved in the air dispersion 

modeling component of the risk analysis process: 

“Although dispersion modeling is a valuable tool for estimating concentration and 

deposition impacts, it has many limitations. The accuracy of the models is limited by 

 The ability of the model algorithms to depict atmospheric transport and dispersion 

of contaminants, and 

 The accuracy and validity of the input data. 

For example, most refined models require input of representative meteorological data 

from a single measuring station. In reality, a release will encounter highly variable 

meteorological conditions that are constantly changing as it moves downwind. U.S. 

EPA’s Guideline on Air Quality Models—Revised (U.S. EPA 1986a, 1993b; Title 51 

CFR Appendix W) describes two types of model uncertainty. Inherent uncertainty 

involves deviations in concentrations that occur even if all of the model input is 

accurate. Reducible uncertainty is associated with the model and the uncertain input 

values that will affect the results. Although it is important to accurately represent 

actual conditions by selecting the right model, and using accurate and representative 

input data, all model results are subject to uncertainty. Nevertheless, models are 

generally considered reasonably reliable in estimating the magnitude of highest 

concentrations resulting from a release, although they may not necessarily be timeand space-specific (Title 51 CFR Appendix W). When applied properly, air 

dispersion models are typically accurate to ± 10 to 40 percent and can be used to yield 

a “best estimate” of air concentrations (Title 51 CFR Appendix W).” 

To capture the variability in meteorological conditions across the United States, air 

dispersion modeling was conducted using data sets from meteorological stations located within 57 

“air regions” across the lower 48 states. Because dispersion also depends on the footprint of the 

source, we addressed variability in the landfill surface areas by choosing 19 representative areas to 

represent the distribution of landfills. Appendix G explains how linear interpolation was used to 

estimate unitized air concentrations for the actual landfills; we also present an analysis that evaluates 

how well we achieved our goal of less than 5 percent interpolation error. 

The choice of receptor locations (discrete points located at various distances and directions 

from the landfill) is considered a major source of uncertainty in the air modeling simulations because 

(1) the distribution of actual resident locations around each site is unknown and (2) individuals move 

through time and space. We adopted a receptor grid approach that was developed to support previous 

listing determinations and risk assessment studies approximately 8 years ago.21 This approach places 

the receptor points at concentric distances at 25, 50, 75, 150, 300, 500, 1000 and 2000 meters from 

the edge of the landfill at 10° radial increments around the full 360°. This approach tends to be 

conservative with respect to risk because the ring distances are much closer together near the edge of 

the landfill; as a result, a high proportion of the receptor locations are closer to the source of 

 

21 For example, the IWAIR model and Air Characteristics Study finalized in 2002 included 6 receptor distances from the unit: 25, 50, 

75, 150, 500, 1000 meters; this is very similar to the distances used in the Dyes and Pigments Listing Determination (U.S. EPA,

2003b). 

Do not cite or quote. 4-20 
Review Draft Section 4.0 Risk Characterization 

emissions where exposure concentrations in ambient air would be expected to be higher relative to 

outer distance rings. 

The approach used in locating receptor points for the ambient air exposure pathway is 

different than the approach used for the groundwater pathway. For groundwater exposures, the 

receptor locations (actually, the receptor wells) are located randomly throughout the lateral extent of 

the plume up to a distance of 1 mile. An analogous approach for the air pathway would have been to 

locate receptors within the dominant air plume; however, unlike groundwater flow, the prevailing 

wind at a given site varies widely in terms of both direction and strength, making such an approach 

impractical, at best. 

To explore the uncertainty associated with receptor location for the ambient air pathway, we 

constructed an air modeling scenario that located receptors in a manner resembling the groundwater 

pathway. For each landfill site, we identified the point of maximum air concentration (from among 

the distances and directions modeled) and assumed that this radial spoke was the centerline of the air 

plume. We further assumed that the highest air concentrations would be found at 10° increments on 

either side of the centerline, thereby creating a 20° air “wedge” that consisted of receptor locations 

along 3 radial spokes and 8 distances (i.e., 24 receptor locations per wedge per site). Finally, we 

randomly sampled air concentrations at these receptor locations across all wedges and all sites for 

the most volatile and least volatile solvents with health benchmarks for the inhalation pathway (i.e., 

tetrachloroethylene, 2-ethoxyethanol). At the 90th percentile of the resulting risk distributions, we 

found that the risks were less than a factor of 2 higher than the risks predicted using radials and 

distances along the full 360°. Given the fact that the close spacing of the rings nearest the landfill 

introduces a conservative bias, the results for the “wedge” scenario suggest that the air pathway 

methodology is appropriately conservative (i.e., does not underestimate risk). 

4.2.2.3 Groundwater Model 

The groundwater pathway model, EPACMTP, assumes that the soil and aquifer are uniform 

porous media. EPACMTP does not model preferential pathways such as fractures, macropores, or 

facilitated transport, which may affect migration of strongly sorbing constituents such as metals. 

EPACMTP does not model colloidal transport or the geochemical interactions between different 

contaminants in the leachate. Any of these factors could result in under predicting contaminant 

concentrations at the receptor well. Conversely, the EPACMTP model incorporates the following 

assumptions: 

 Transverse dispersion is negligible in the unsaturated zone, potentially resulting in an 

overestimation of risks 

 Receptors use the uppermost aquifer, rather than a deeper aquifer, as a domestic source of 

drinking water, which overestimates risks where the uppermost aquifer is not used 

 Hydrogeologic conditions that influence contaminant fate and transport are uniform spatially 

as well as temporally (that is, in the time period over which the model is executed, 10,000 

years), potentially resulting in an underestimation or overestimation of receptor well 

concentrations. 

In modeling the fate and transport of chemicals in groundwater, complex hydrogeologic 

environments, such as karst or highly fractured aquifers, were not assessed. Some fraction of the 

groundwater settings in this analysis may have fractured flow. In general, fractured flow in 

groundwater can channel the contaminant plume, thus allowing it to move faster and in a more 

Do not cite or quote. 4-21 
Review Draft Section 4.0 Risk Characterization 

concentrated state than in a non-fractured flow environment. As a result, the modeling may under- or 

overestimate the concentrations in the groundwater. 

Finally, there is uncertainty in predicting the movement of contaminants over long periods of 

time. The risk to receptors for the groundwater pathway was evaluated over a time period of 10,000 

years. There are significant uncertainties concerning how exposure and environmental assumptions 

will change over time, and the modeling methodology does not change these assumptions over this 

10,000-year period. 

It should be pointed out that EPACMTP, its predecessors, and its derivatives have been 

verified extensively during the past decade at each of the developmental stages. The model has been 

verified, in numerous cases, by comparing the simulation results against both analytical and 

numerical solutions. Additionally, EPACMTP and its predecessors have been validated using actual 

site data from four different sites. The relevant verification and validation history is discussed in 

detail in Appendix D of the EPACMTP Technical Background Document (U.S. EPA, 2003c). A 

brief summary of the verification history follows: 

 The preliminary verification of EPACMTP was performed by U.S. EPA Office of Research 

and Development in 1992. 

 Following the preliminary verification, detailed module-level verification was conducted on 

the flow and transport sub-modules of the vadose-zone and the aquifer modules during the 

period 1993 to 1994. The modules were verified against analytical solutions, and numerical 

solutions from a number of well-documented simulators. 

 In 1997, the EPACMTP code was verified utilizing a testing plan developed according to 

ASTM standards. The vadose-zone and the aquifer modules, as well as the composite model 

(based on the sequentially linked vadose-zone and aquifer modules), were verified against 

analytical and numerical solutions. 

 In 1999, the vadose-zone and aquifer modules were extracted from EPACMTP to be 

included as part of the multimedia, multi-receptor, and multi-pathway risk assessment 

(3MRA) software system. The flow and transport sub-modules of both modules were verified 

against the results from EPACMTP. 

 A pseudo-3D aquifer module was developed for the 3MRA software system. An exhaustive 

verification was conducted of all of the components in the extracted vadose-zone module and 

the new pseudo-3D aquifer module in 2000. The modules were verified against analytical 

solutions and EPACMTP results. 

4.2.2.4 Exposure Model 

The Exposure Model calculates the drinking water ADD as a function of several exposure 

factors, including body weight, consumption of drinking water, and exposure duration that vary with 

age. This is a very simple construct used by many programs at EPA (e.g., Superfund; Office of 

Water) to estimate dose assuming a constant exposure point concentration. Receptors are implicitly 

assumed to spend all of their time at the receptor point locations, introducing a conservative bias into 

the risk results. 

Similarly, the dermal absorbed dose (DAD) is calculated to estimate dermal dose as a 

function of exposure factors, including the ratio of skin surface area available for contact (SA) to 

body weight, event duration, and exposure duration. A key assumption made in our DAD 

Do not cite or quote. 4-22 
Review Draft Section 4.0 Risk Characterization 

calculations was that 100 percent of the skin surface area was available for dermal contact. This 

assumption will in most cases result in an overestimation of dose. RAGS Part E (U.S. EPA, 2004b), 

provides an overview of the uncertainties associated with the dermal exposure analysis process. In 

this discussion, general statements are made about the uncertainty associated with key parameters. In 

general, it is expected that the human exposure parameters used in the DAD calculation will be 

associated with low or medium uncertainty. The values used in our analysis were based on EFH and 

Child-Specific EFH data and are discussed in Section 4.2.3.8. One key input parameter that is 

identified as being associated with a high level of uncertainty is the chemical-specific permeability 

coefficient (i.e., Kp term used in the calculation of DA_event). This parameter and its contribution to 

uncertainty in a dermal analysis are discussed in Section 4.2.3.6. 

4.2.2.5 Risk Model 

The Risk Model essentially compares the predicted dose or air concentration to a human 

health benchmark, represented as a point estimate. Although the health benchmarks are derived with 

some consideration of intraspecies variability in toxicological sensitivity, the Risk Model does not 

explicitly simulate these differences. Given the differences in child sensitivity to chemicals and the 

emerging research on latency of effects, the lack of child-specific treatment of health effects should 

be recognized as a limitation of the risk model. 

4.2.3 Parameter Uncertainty 

As described in the Problem Formulation, the Monte Carlo approach was designed to capture 

the variability in landfill characteristics and environmental setting across the United States. To that 

end, we identified and developed distributions for most of the parameters required by the models. 

However, despite the significant effort involved in developing the input data, parameter values are 

associated with varying degrees of uncertainty. Uncertainty in parameter estimates refers to a variety 

sources including, for example, (1) random or systematic measurement errors such as errors in 

measuring the octanol-water partition coefficient; (2) using surrogate data to represent actual 

parameter values such as using soil half lives as a measure of the anaerobic biodegradation potential; 

(3) the representativeness of the data such as using a constructed data set to represent reasonable 

landfill characteristics rather than using actual landfill data; and (4) the variability in parameters that 

might change over time, space, or some population of individuals such as the variability in 

toxicological response. In addition, there can be significant uncertainties associated with the state of 

knowledge regarding data dependency because we are unable to identify and/or quantify 

relationships between and among input parameters that are correlated in some way. 

In particular, the spatial and temporal variability in parameters relevant to predicted 

exposures has a significant influence on the distribution of exposure and, ultimately, risks estimated 

for individuals. For example, the rainfall or precipitation rates used to calculate infiltration and 

recharge to groundwater are measured daily by the National Weather Service at many locations 

throughout the United States; statistics about these parameters are well documented. Although the 

distributions of these parameters may be well known, their actual values vary spatially and 

temporally and cannot be predicted exactly. Thus, the annual average infiltration rates used in the 

LFCR model for a particular climate station will provide information on average (not actual) 

conditions appropriate for this analysis. In addition, using data from multiple climate stations located 

throughout the United States can account for some, but not all, spatial variability. 

Do not cite or quote. 4-23 
Review Draft Section 4.0 Risk Characterization 

The remainder of this section describes how parameter variability (i.e., Type A uncertainty) 

was addressed in the analysis, and discusses the potential implications of this uncertainty for the 

analysis results. 

4.2.3.1 Climate Parameters 

The EPACMTP data system provided infiltration and liner leakage rate estimates. As 

discussed in Sections 2 and 3, EPACMTP applies a site-based, regional Monte Carlo system to 

represent variability within and between landfills in the United States. This approach has been well 

documented, peer reviewed, and used in many risk analysis applications (e.g., U.S. EPA, 2003b). 

The general range of infiltration estimates (Table F-5 in Appendix F) compares well with other 

landfill recharge estimates (e.g., Bartelt-Hunt et al., 2006); however, the degree of uncertainty in the 

estimates cannot be characterized quantitatively without more extensive data. Model uncertainty 

exists with respect to the HELP model used to estimate infiltration rates within EPACMTP. 

However, the HELP model is well established, and the EPACMTP parameterization is well 

documented and rigorous. The EPACMTP liner leakage rates are based on empirical data (Tetra 

Tech, 2001). The underlying data set is relatively small, resulting in greater uncertainty in the 

estimates. Nevertheless, no known bias exists in the EPACMTP infiltration or leaching rate 

estimates. 

4.2.3.2 Cover/Liner Characterization Parameters 

Parameters characterizing the landfill cover and liner include soil moisture content, porosity, 

thickness, and geomembrane permeation coefficient. The EPACMTP data system provided soil 

moisture content and porosity estimates. As described in Section 3.1.2.2, EPACMTP incorporates a 

distribution of three soil types to represent national variability in soil conditions. The resulting 

ranges for moisture content (0.19–0.34 volume water/volume total) and porosity (0.41–0.45) 

compare well with those from other sources (e.g., Benson et al., 1999; Bartelt-Hunt et al., 2006). The 

uncertainty associated with the EPACMTP soil parameter estimates cannot be evaluated 

quantitatively without more extensive data; however there is no known bias in the estimates. 

The municipal landfill cover and liner thicknesses were based on Subtitle D regulatory 

requirements. Geomembrane permeation coefficients were based on laboratory-measured values and 

estimates from a published, empirical correlation. Given the range of literature values, the reported 

dependence on concentration, and the estimation from an empirical correlation, geomembrane 

permeation coefficients are uncertain, leading to uncertainty in the degree of diffusion through 

composite liners. However, there is no known bias in the estimates. 

Sorption to cover/liner soil layers can be described in the LFCR model by adjusting the 

diffusion coefficient (specifically, dividing the diffusion coefficient by a retardation factor). 

However, for this analysis, it was assumed that sorption is inactive within cover/liner soil layers. 

This assumption is due to the absence of a generalized approach to estimate the retardation 

coefficient for national conditions, as well as significant uncertainties and unresolved questions in 

the science of transport within cover/liner soils (see Section 3.1.1.2). Neglecting sorption in the 

cover/liner soil layers may provide a conservative estimate (overestimated mass loss). However, the 

extent of this potential bias is unknown given the complexity and absence of a generalized modeling 

approach. 

Do not cite or quote. 4-24 
Review Draft Section 4.0 Risk Characterization 

The cover/liner characterization parameters influence the degree of diffusion through the 

liner or cover. Although some of the parameters are uncertain (e.g., geomembrane permeation 

coefficient), the relative mass loss via composite liner leakage versus diffusion in the results is 

consistent with results reported in the literature (Park et al. 1996; Edil, 2003; Foose 2002). In the 

LFCR results, diffusive transport through the liner exceeds leakage by up to three orders of 

magnitude. Several researchers have documented the importance of diffusion through 

geomembranes and emphasized that this transport mechanism can be much more significant than 

liner leakage for many VOCs (Park et al. 1996; Edil, 2003; Foose 2002). Park et al. (1996) 

demonstrated that the diffusive mass flux through a geomembrane can be more than two orders of 

magnitude greater than the mass flux due to the typical number and size of holes found in landfill 

geomembranes. The analysis in Foose (2002) demonstrated that diffusive fluxes for many VOCs can 

be up to six orders of magnitude greater than the leakage. Therefore, the industrial solvents analysis 

result that diffusion through the geomembrane can exceed leakage by up to three orders of 

magnitude is consistent with the literature. 

4.2.3.3 Hydrogeologic Setting Parameters 

The EPACMTP data system provided the hydrogeologic setting parameters, including the 

depth to groundwater and unsaturated zone soil moisture content and porosity. The development of 

correlated hydrogeologic environment data (e.g., unsaturated zone thickness, hydraulic conductivity) 

relied on a hydrogeologic database that included data from approximately 400 hazardous waste sites 

organized into 12 hydrogeologic environments. Each of the 66 Industrial D landfill locations was 

classified into one of the 12 hydrogeologic environments, allowing associated correlated parameter 

values to be sampled. This approach assumed that these 12 hydrogeologic environments and the 

associated correlated parameter distributions provide an accurate representation of nationally 

variable landfill conditions. 

The uncertainty associated with these parameters is similar to the uncertainty described 

above for the EPACMTP-based climate and cover/liner parameters. These parameters influence the 

degree of diffusion to groundwater with no liner, which accounts for a relatively small percentage of 

the mass loss, and only in the no-liner scenario (1 percent, on average). This fact coupled with the 

relative dominance of aqueous advective mass transfer to groundwater in the no liner scenario 

suggests that the uncertainty associated with these parameters has a relatively small impact on the 

results. 

4.2.3.4 Landfill Characterization Parameters 

Landfill area, height, and operating life were derived primarily from the EPA-WBJ 

database—the most up-to-date and complete resource available and identified for use in 

characterizing municipal landfills—as described in Section 3.1.2.4. The resulting distributions 

compare well with other recent data sources (see Table 3-2), although some differences were 

apparent. The degree of uncertainty cannot be evaluated quantitatively, given the absence of more 

accurate and comprehensive landfill characterization data and, given the somewhat complex 

relationship among these parameters, understanding the influence of individual parameters is not 

straightforward. For example, increasing the landfill area-to-height ratio (while holding other factors 

constant) increases the rate of mass loss (more surface area for transport per volume). However, 

greater depths are associated with more mass in the landfill and thus more mass release overall. 

Do not cite or quote. 4-25 
Review Draft Section 4.0 Risk Characterization 

Also, the operating life and volume contribute to the rate of mass release for a given landfill. 

Therefore, the influence of these parameters cannot be characterized simply without more extensive 

sensitivity analysis. 

Other landfill characterization parameters (landfill cell operating life, time before final cover, 

lift height, fraction of day without a cover, working face angle) are associated with landfill 

management practices. The associated parameterization relied strongly on consultation with experts 

in the field (Barlaz, 2006; Bartelt-Hunt, 2006). The value ranges, distributions, and correlations were 

designed to represent the population of municipal landfills in the contiguous United States, including 

the variability and relationships across these characteristics. Lacking raw data to compare with the 

representative data, it was not feasible to quantify the uncertainty in the landfill characterization data 

set; however, there is no reason to expect that the representative data set created for this analysis 

contains any systematic bias. 

4.2.3.5 Waste Characterization Parameters 

 Waste characterization parameters include average waste temperature, bulk density, moisture 

content, porosity, fraction organic carbon, gas generation rate, and potential methane generation 

capacity. The waste characterization parameterization was based on literature review and 

consultation with experts in the field (Barlaz, 2006; Bartelt-Hunt, 2006). The value ranges, 

distributions, and correlations were designed to provide a reasonable representation of the variability 

in these characteristics and the probable relationships between parameters (e.g., porosity is inversely 

correlated with bulk density). Although the uncertainty cannot be evaluated quantitatively given the 

absence of more comprehensive data, no known bias exists in the waste characterization parameters. 

4.2.3.6 Chemical Parameters 

Several chemical parameters influence source model results, including aqueous-phase 

diffusivity, vapor-phase diffusivity, first-order biodegradation rate, first-order abiotic degradation 

rate (hydrolysis), Henry’s law constant, and organic carbon partition coefficient. The variability and 

uncertainty associated with these parameters is discussed below. 

As described in Appendix B, estimation methods were used to calculate diffusivities from 

other chemical parameters (e.g., boiling point, critical temperature). The underlying parameters are 

typically based on laboratory measurements and reliable data sources. The estimation methods are 

well established. Therefore, the diffusivities are estimated with relatively less uncertainty. 

Appendix B describes the sources for first-order biodegradation rate values. Biodegradation 

varies significantly given changes in such local conditions such as bacterial populations, substrate 

characteristics (biodegradability of the waste), temperature, and moisture. In addition, 

biodegradation rates measured in the laboratory can differ significantly from those measured in field 

conditions. The primary data sources for biodegradation rates (e.g., Howard et al., 1991; Aronson 

and Howard, 1997) often relied strongly on laboratory data, and the estimates were not developed 

specifically for landfills. Given these significant sources of variability and uncertainty, conservative 

values were selected for biodegradation rates (longer half-lives). Given the significance of 

degradation for many of the chemicals analyzed (average mass percentage of approximately 41 

percent, and a range from 0 to 99.3 percent), this fate mechanism contributes significantly to the 

uncertainty of the results. 

Do not cite or quote. 4-26 
Review Draft Section 4.0 Risk Characterization 

Abiotic degradation rates represented hydrolysis (see Appendix B). Although some 

variability and uncertainty are associated with this parameter, the relative magnitude of mass loss 

associated with hydrolysis was insignificant relative to biotic degradation (abiotic degradation rates 

were orders of magnitude slower than biodegradation rates). 

Henry’s law constants and organic carbon partition coefficients were based on established 

literature values and subject to less variability and uncertainty than other parameters. 

Chemical-specific permeability coefficients (Kp) are used in estimating dermal exposure 

through contact with contaminated groundwater. RAGS Part E (U.S. EPA, 2004b) states that 

permeability coefficients have been identified as a major contributor to parameter uncertainty in the 

analysis of dermal water exposure. In accordance with guidance in RAGS, we predicted Kp values 

based on chemical-specific log Kow and molecular weight. This approach is recommended in RAGS 

for all organic chemicals to ensure consistency between Agency risk assessments in estimating the 

dermally absorbed dose from water exposures. For halogenated chemicals and other chemicals with 

large molecular weights, RAGS adds a note of caution regarding the estimation of Kp. Specifically, 

they note that Kp would be expected to be underestimated. For this reason, the Agency is exploring 

the use of a new Kp correlation based on molar volume and logKow. 

4.2.3.7 Variability of Source Model Outputs 

Section 3.1.3 (Tables 3-5 and 3-6) provides the relative percent contribution (of the total 

chemical mass added) for each of the primary transport pathways evaluated, summarized by 

chemical and liner scenario. These tables also provide the maximum and minimum percent 

contributions for all of the simulations. Comparing the range of percent contributions gives an 

indication of the variability associated with the parameters underlying the transport processes (e.g., 

degradation relies on the degradation rate; diffusion relies on diffusivities and cover/liner 

characteristics). The most significant variability for all chemicals and liner scenarios is associated 

with degradation (a range of contribution from 0 to 99.3 percent, on average 41 percent). Other 

transport mechanisms (aqueous advection, vapor advection, diffusion to atmosphere) exhibit 

significant variability, with ranges from 0 to greater than 90 percent. However, vapor advection is 

the only mechanism other than degradation that contributes 10 percent or greater on average. 

Diffusion to groundwater exhibits somewhat less variability (0 to 56 percent) and contributes 

relatively less to the overall mass loss (approximately 1 percent, on average). 

There are significant differences in results, depending on the specific chemical and physical 

properties of the constituent. For example, for cresols, the average mass loss through vapor 

advection is less than 0.1 percent, whereas for tetrachloroethylene, the average mass loss is close to 

70 percent through this pathway (the difference is primarily due to the much greater volatility of 

tetrachloroethylene). Although a formal sensitivity analysis would provide more quantitative 

information regarding the contribution of different parameters to the variance in the results, these 

tables indicate which transport pathways are most significant and contribute the most variability to 

the source modeling results. 

Do not cite or quote. 4-27 
Review Draft Section 4.0 Risk Characterization 

4.2.3.8 Human Exposure Factors 

Exposure modeling relies heavily on default assumptions concerning population activity 

patterns, mobility, dietary habits, body weights, and other factors. The probabilistic analysis for the 

adult and child exposure scenario addressed the possible variability in the exposure modeling by 

using statistical distributions for these variables for the adult and child residents. For all exposure 

factors varied, a single exposure factor distribution was used for adults for both males and females. 

For child exposures, one age (age 1) was used to represent the age at the start of exposure, because 

this age group is considered to be most sensitive for most health effects. 

The Exposure Factors Handbook (U.S. EPA, 1997c,d,e) provides the current state of the 

science concerning exposure assumptions, and it was used throughout this analysis to establish 

statistical distributions of values for each exposure parameter for each receptor. For most of the 

human exposure factors, distributions were fitted to the data summaries from the Exposure Factors 

Handbook or the Child-Specific Exposure Factors Handbook. The approach most often taken was to 

fit these distributions to selected percentiles rather than to the raw data. This approach implies that 

fitting to percentiles will produce essentially the same distribution as fitting to raw data. The chisquare goodness-of-fit test was used to select the most appropriate statistical distribution for most of 

the exposure factors. However, all of the statistics that were used to assess goodness of fit assume a 

random sample, which may or may not be a valid assumption for data presented in the EFH’s, 

because those data are based on surveys that, in many cases, do not involve purely random samples 

(e.g., they use clustering and stratification, primarily for economic reasons). For a national Monte 

Carlo simulation, this is considered to be a relatively minor—albeit unquantified—source of 

uncertainty. 

4.2.3.9 Human Health Benchmarks 

There are uncertainties inherent in the development of the toxicity values, or human health 

benchmarks, used in this risk assessment. Uncertainties generally associated with human health 

benchmarks are discussed in detail in EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 

2005b), Methods for Derivation of Inhalation Reference Concentrations and Application of 

Inhalation Dosimetry (U.S. EPA, 1994a), and IRIS (U.S. EPA, 2007). Some uncertainties may be 

considered quantitatively, while others are generally expressed in qualitative terms. The reader may 

note that ”the primary goal of EPA actions is protection of human health; accordingly, as an Agency 

policy, risk assessment procedures, including default options that are used in the absence of 

scientific data to the contrary, should be health protective.” (U.S. EPA, 2005b Cancer Guidelines, 

pages 1-7). 

Chronic noncancer reference values (RfCs and RfDs) represent chronic exposure levels that 

are intended to be health-protective levels. Specifically, these values provide an estimate (with 

uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous 

inhalation exposure (RfC) to the human population (including sensitive subgroups) that is likely to 

be without an appreciable risk of deleterious effects during a lifetime.22 To derive values that are 

intended to be “without appreciable risk,” the methodology relies on an uncertainty factor approach 

that includes consideration of both uncertainty and variability. When there are gaps in the available 

information, uncertainty factors are applied to derive reference values that are intended to be 

protective against appreciable risk of deleterious effects. Uncertainty factors are commonly default 

 

22 See IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm). 

Do not cite or quote. 4-28 
Review Draft Section 4.0 Risk Characterization 

values23 (e.g., factors of 10 or 3), used in the absence of compound-specific data; where data are 

available, uncertainty factors may also be developed using compound-specific information. When 

data are limited, more assumptions are needed and more uncertainty factors are used. Thus, there 

may be a greater tendency to overestimate risk, in the sense that further study might support 

development of reference values that are higher (i.e., less potent) because fewer default assumptions 

are needed. However, for some pollutants, it is possible that risks may be underestimated. 

While collectively termed “uncertainty factors,” these factors account for a number of 

different quantitative considerations when using observed animal (usually rodent) or human toxicity 

data in the development of the reference concentration. The uncertainty factors are intended to 

account for (1) variation in susceptibility among the members of the human population (i.e., interindividual variability); (2) uncertainty in extrapolating from experimental animal data to humans 

(i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with 

less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty 

in extrapolating the observed data to obtain an estimate of the exposure associated with no adverse 

effects; and (5) uncertainty when the database is incomplete or there are problems with the 

applicability of available studies. 

 Cancer unit risk estimate (URE) values used are those that have been developed to generally 

provide an upper-bound estimate of risk. That is, they represent a “plausible upper limit to the true 

value of a quantity” (although this is usually not a true statistical confidence limit).24 In some 

circumstances, the true risk could be as low as zero; however, in other circumstances, the risk could 

also be greater.25 When developing an upper-bound estimate of risk and to provide risk values that 

do not underestimate risk, health-protective default approaches are generally used. EPA typically 

uses the upper-bound estimates rather than lower-bound or central tendency estimates in our risk 

assessments, an approach that can have limitations for other uses (e.g., priority setting or expected 

benefits analysis). 

The hierarchy of sources that was implemented for this analysis (U.S. EPA, 2003f) was 

based largely on the rigor of review that a benchmark has received. In addition to the strength of the 

review process, confidence in human health benchmarks also can be associated with the date the 

benchmarks were derived (or last reviewed). As additional toxicological data become available, the 

database becomes more complete. Methodologies evolve over time, with improvements in existing 

methods (e.g., EPA’s 1994 RfC methodology) and the development of new health benchmark 

practices (e.g., benchmark dose methodology). Therefore, there is less confidence associated with 

older benchmarks. 

The EPA IRIS assessment for benzene provides a range of plausible unit risk estimates. For 

inhalation unit risk estimates, the high end of the range is 7.8E-06 per μg/m3

 and the low end of the 

range is 2.2E-06 per μg/m3. For oral exposures, the cancer slope factors that are derived from these 

unit risk estimates range from 5.5E-02 per mg/kg-day at the high end of the range to 1.5E-02 per 

 

23 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, 

based on general scientific knowledge and policy judgment, that are applied to various elements of the risk-assessment process when 

the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing 

the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the 

absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency 

may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with 

EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not 

underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk 

Assessment Principles and Practices, EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf . 24 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm). 

25 An exception to this is the URE for benzene, which is considered to cover a range of values, each end of which is considered to be 

equally plausible, and which is based on maximum likelihood estimates. 

Do not cite or quote. 4-29 
Review Draft Section 4.0 Risk Characterization 

mg/kg-day at the low end of the range. This assessment used both the high and low ends of the range 

for both inhalation and oral exposures, and thus two separate estimates are presented for benzene 

cancer risks. 

Most health benchmarks used in this analysis were from IRIS. Human health benchmarks in 

IRIS have been subjected to rigorous internal and external reviews and represent EPA-wide 

consensus human health risk information. However, some benchmarks in IRIS are quite dated (e.g., 

the RfD for n-butanol was derived in 1990). The Provisional Peer-Reviewed Toxicity Values derived 

by the Superfund Technical Support Center have been peer reviewed, but have not undergone EPA’s 

formal review process, and therefore, do not represent verified benchmarks. ATSDR and CalEPA 

human health benchmarks have been subject to expert scientific and public review; the derivation of 

these benchmarks is transparent and publicly available. The health benchmarks in HEAST have not 

undergone as rigorous a review as those in IRIS and do not represent EPA-wide consensus 

information. HEAST has not been updated since 1997 and may not provide the most current source 

of information on some contaminants. Although every effort has been made to identify peerreviewed reference values for cancer and noncancer effects for the constituents, some constituents 

have no such values or lack values for certain exposure routes. In those cases, an understatement of 

risk for these constituents is possible. Finally, it should also be noted that the toxicologies of 

trichloroethylene and tetrachloroethylene (perchloroethylene) are in different stages of an ongoing 

EPA review process being conducted in cooperation with the National Academy of Sciences. For 

tetrachloroethylene, we used both high and low end values for the inhalation unit risk estimates, 

which resulted in two separate estimates for tetrachloroethylene cancer risks. We used the CalEPA 

value (5.9E-06 per μg/m3

) as a higher end risk factor. For the lower end value we used an inhalation 

unit risk estimate (7.1E-7 per μg/m3

) published by EPA’s Office of Pollution Prevention and Toxic 

Substances (U.S. EPA 1998) that was based on the same study (by the National Toxicology Program 

in 1986) as the CalEPA inhalation value. 

Chemical-specific health benchmarks were used for all constituents. However, the RfD for 

m- and o-cresol was used as a surrogate for the cresol mixture assessed in the analysis; this is not 

thought to have introduced any significant uncertainty. Human health benchmarks are not agespecific and were therefore applied to both child and adult receptors, thereby introducing some 

uncertainty. 

Although receptors are likely to be exposed to mixtures of solvents, noncancer hazard and 

cancer risk were assessed separately for each constituent. Additivity across chemicals was not 

considered in the risk model; neither was synergism or antagonism. However, for noncarcinogens, 

HQ estimates were combined for chemicals having RfDs and RfCs with matching endpoints and no 

portal-of-entry effects (benzene, cresols, and methyl ethyl ketone). The CalEPA RfC for mixed 

cresols was derived based on the same oral study as the IRIS RfD for o- and m-cresol (route-to-route 

extrapolation). The IRIS RfC and RfD for benzene were based on the same inhalation study. The 

IRIS RfC and RfD for methyl ethyl ketone were based on separate, route-specific 

reproductive/developmental studies, demonstrating that the developing fetus is the target organ for 

methyl ethyl ketone regardless of administration route. Additivity across routes of exposure for the 

same chemical was assumed; however, greater than or less than full additivity may occur (e.g., if a 

metabolism pathway is overwhelmed and more parent compound is excreted from the body 

unchanged). Noncancer hazard may, therefore, have been under- or overestimated. For carcinogens, 

cancer risks across groundwater exposure pathways were combined for adults (i.e., ingestion of 

drinking water, dermal contact with groundwater, and inhalation of shower air) and children (i.e., 

ingestion of drinking water and dermal contact with groundwater), irrespective of the type of cancer 

caused by each route of exposure, possibly introducing some uncertainty in the mass loading rates. 

Do not cite or quote. 4-30 
Review Draft Section 4.0 Risk Characterization 

In the case of dermal noncancer hazards and cancer risk, estimates were made using the 

unadjusted RfD and oral CSF benchmarks. This assumption of 100 percent absorption may result in 

an overestimation of human health impacts from dermal contact during a bathing or showering 

event. However, the dermal route of exposure can result in different patterns of distribution, 

metabolism, and excretion than occur from the oral route. 

There are uncertainties inherent in both the human exposure factors and human health 

benchmarks used in this analysis. Uncertainty is introduced by using probability distribution 

exposure durations together with lifetime average daily dose and RfD/RfC values. Assuming that the 

population parameters (e.g., exposure duration, body weight, consumption rates) underlying the 

derivation of the RfD/RfC values are consistent with the probability distributions for exposure 

duration used in the exposure analysis may introduce further uncertainty. However, in keeping with 

EPA’s goal of protecting public health and the environment, default assumptions are used to ensure 

that risk to chemicals is not underestimated and thus health benchmarks are designed to account for 

uncertainty by not underestimating risk. 

Do not cite or quote. 4-31 
Review Draft Section 5.0 Landfill Mass Loading Rates 

5.0 Landfill Mass Loading Rates 

The objective of the wipes risk assessment was to calculate chemical-specific mass loading 

limits (in kg solvent/yr) that reflect an annual quantity of a constituent that can be placed in a landfill 

and be protective of human health. These mass loading limits are defined as mass loadings that result 

in cancer risks at the specified threshold (1 in 100,000) and noncancer HQs no greater than 1 at 

specified percentiles of the risk distribution (i.e., 50th and 90th percentiles). 

This section presents the mass loading limit results, as well as a summary of the steps used to 

estimate the mass loading limits. Many of the calculations presented in this section rely on the 

linearity of the modeling system, whereby the results (mass flux from source, receptor 

concentrations, and risks) are linearly proportional to the input chemical concentration in the waste. 

The linearity of the system allows the use of linear scaling factors to adjust the inputs and outputs to 

calculate risk-based mass loading limits. These calculations are described in Section 5.1. 

Section 5.2 presents the mass loading limits associated with each scenario analyzed for the 

90th and 50th percentiles of the risk distribution. Each scenario represents a specific chemical, 

receptor (adult, child), pathway (drinking water ingestion, shower inhalation, ambient air inhalation), 

endpoint (cancer, noncancer), and liner type (no liner, composite liner). 

5.1 Summary of Risk Analysis Calculations 

The process used to calculate the mass loading limits can be summarized by the following 

steps: 

1. Using a constant initial concentration26 for each solvent, we run the source model and 

produce a distribution of chemical releases (e.g., flux) to air and groundwater that reflect the 

variability in landfill and environmental characteristics across the nation. The source model 

simulation also produces 10,000 mass loading rates (MLRs) as function of the solvent 

concentration and the landfill characteristics (e.g., landfill depth, surface area). 

2. The risk metric for this analysis is a mass loading rate rather than a solvent concentration. 

Therefore, we normalized the outputs from the source model to a constant value by multiplying 

the result for each realization by a normalization factor. The normalization factor is simply the 

unitized rate (1 kg/yr) divided by the actual MLR for that realization. The source model is linear 

with respect to both concentration and MLR, and therefore, we produce a set of 10,000 source 

model results that are used as input for the groundwater and air models for each solvent/scenario. 

The solvent releases predicted for each individual realization are based on a constant MLR of 1 

kg/yr. 

3. We then take the normalized source model results and run the simulation through to risk, 

which includes the fate and transport models, the exposure models, and the risk model. For each 

scenario, the resulting distribution of risk is sorted and we identify the risk (or hazard) result at 

selected percentiles of the distribution, notably the 90th and 50th. 

4. For the 90th percentile risk, we calculate a scaling factor to derive the MLR that corresponds 

exactly to the cancer risk values evaluated (1E-4, 1E-5, or 1E-6) and/or an HQ of 1. The scaling 

factor is simply the ratio of the risk/hazard criteria to the risk/hazard estimate at the 90th 

 

26 Using a fixed concentration allowed us to take advantage of QA/QC routines that are built into the model; however, we could have 

simply run the source model using a fixed mass loading rate. 

Do not cite or quote. 5-1 
Review Draft Section 5.0 Landfill Mass Loading Rates 

percentile (e.g., for cancer, this is the risk level specified by the risk criteria divided by the 

estimated risk). 

5. We then multiply the constant MLR of 1 kg/yr by the scaling factor at the selected percentile. 

Because the MLR is a constant value of 1 kg/yr, the scaling factor is equivalent to the MLR at 

the percentile of interest (i.e., 1 x SF = SF = MLR). The MLR can be derived for any percentile 

of interest; however, the risk-based MLRs were derived at the 90th and 50th percentiles of the 

distribution per EPA policy for this analysis. 

6. The lowest MLR represents that value that will protect at least 90 (or 50) percent of the 

hypothetical individuals (all receptor types) living within 2 km of a municipal solid waste landfill 

for all exposure pathways. As a check, these MLRs could be used as an input to the model to 

generate a distribution of risk estimates with the 90th and 50th percentiles corresponding to a 

cancer risk level (1E-04, 1E-05, or 1E-06) or noncancer hazard level (HQ) of 1. This MLR is 

referred to as the Risk-Based Mass Loading Limit (RB-MLL) 

7. Lastly, we perform a check on the MLR to ensure that solubility limits are not exceeded (e.g., 

the MLR should not result in pure product in the landfill). 

These steps are described in more detail in the following paragraphs. 

Step 1. Run the Source Model Using a Constant Initial Concentration 

The landfill source model analysis included 10,000 simulations (realizations) per chemical 

and landfill liner scenario, which were designed to reflect the variability in national landfill 

conditions (e.g., climate, geometry, waste characteristics). With 20 chemicals of interest and 2 liner 

scenarios, the source modeling included 400,000 simulations. Each simulation produced a 200-year 

time series of annual mass loss to air and groundwater from the landfill. See Section 3.1 for further 

details about the source modeling. 

The source model simulations used a constant initial solvent concentration in the landfill 

waste (1 kg solvent/m3 waste). Because the simulated landfills have different volumes and operating 

life spans (to represent national variability), the chemical mass loading rate varies in the raw source 

model results. The simulation-specific mass loading rate was calculated as follows: 

lf

totin

i T

VC LR× = (Equation 5-1) 

where 

 LRi = simulation-specific loading rate for ith simulation (kg/yr) 

 Cin = initial total chemical concentration in landfill waste (1 kg/m3) 

 Vtot = simulation-specific landfill volume (m3) 

 Tlf = simulation-specific landfill operating life (yr). 

Step 2. Normalize the Source Model Results to a Constant Mass Loading Rate 

Because the objective of the analysis was a mass loading limit rather than a concentration 

limit, the raw source model results were normalized to reflect a constant mass loading rate for all 

landfills. This normalization was accomplished by multiplying the source model results (mass loss 

per year to groundwater and air) by the following simulation-specific normalization factor: 

Do not cite or quote. 5-2 
Review Draft Section 5.0 Landfill Mass Loading Rates 

i

unit

i LR

LR NF = (Equation 5-2) 

where 

 NFi = normalization factor for ith simulation (unitless) 

 LRi = simulation-specific loading rate for ith simulation (kg/yr) 

 LRunit = unit mass loading rate = 1 kg/yr. 

With this adjustment, the source modeling results were all based on a constant mass loading rate of 1 

kg solvent/yr. 

Step 3. Calculate Risks and Noncancer HQs Based on the Unitized Mass Loading Rate 

A distribution of risk estimates for each scenario (solvent, liner type, contact medium, 

exposure route, receptor) was then calculated as described in Section 3. Calculating risk from the 

source modeling results involves fate and transport modeling (Section 3.2, air dispersion modeling; 

Section 3.3, groundwater modeling; and Section 3.4, shower modeling), exposure modeling 

(Section 3.5), and health effects modeling (Section 3.6). 

Step 4. Derive the Scaling Factors Needed to Calculate the Risk-Based Mass Loading Rates 

The scaling factor is the ratio of the risk or HQ criteria (i.e., risk of 1E-05 or HQ of 1) to the 

modeled risk or HQ for each simulation. This scaling factor was then used to calculate the mass 

loading rate for each simulation: 

i

criteria

i

Risk

Risk

SF = (Equation 5-3) 

where 

 SFi = scaling factor for the ith simulation, used to calculate mass loading rates (unitless) 

Riskcriteria = risk or HQ criteria (1E-4, 1E-5, or 1E-6 for cancer or HQ = 1 for noncancer) (unitless) 

 Riski = modeled risk for the ith simulation (unitless). 

Step 5. Calculate the Risk-Based Mass Loading Rates 

The mass loading rates for each simulation are equal to the scaling factor values as shown in 

the following equation: 

LR SF LR iiu = × nit (Equation 5-4) 

where 

 LRi = mass loading rate for the ith simulation (kg/yr) 

 SFi = scaling factor used to calculate mass loading rate for the ith simulation (unitless) 

 LRunit = unit mass loading rate = 1 kg/yr. 

This calculation relies on the fact that the source model results were normalized to a constant mass 

loading of 1 kg/yr. The results represent the risk-based mass loading rates for each simulation. 

Do not cite or quote. 5-3 
Review Draft Section 5.0 Landfill Mass Loading Rates 

Step 6. Select the Limiting Pathway for Selected Risk Percentiles 

The limiting pathway is the pathway/receptor/endpoint combination (e.g., ingestion of 

drinking water/child receptor/cancer endpoint) with the smallest (most limiting) mass loading rate at 

the selected risk percentiles (i.e., 50th and 90th) for each of the 40 solvent/liner combinations. The 

resulting mass loading limit is associated with the pathway/receptor/endpoint combination with the 

highest risk for each solvent/liner combination. A risk-based mass loading rate at the risk criteria 

established by EPA (e.g., the 90th percentile of the distribution) is referred to as a Risk-Based Mass 

Loading Limit (RB-MLL). 

Step 7. Check Whether the Mass Loading Limits Exceed Solvent Partitioning Limitations 

Because the model predicts extremely low levels of exposure and risk for a number of the 

solvents, some of the risk-based mass loading rates exceeded reasonable limits with respect to 

solvent partitioning in the landfill. Specifically, if the concentration of a chemical at the mass 

loading limit would on average exceed the solubility of that chemical, the result was flagged and an 

alternative solubility-based mass loading limit was used. The following equation was used to 

calculate the solubility-based mass loading limits: 

avglf

avgwsol avgtot

sol T

VRC

LR

,

× , × , = (Equation 5-5) 

where 

 LRsol = solubility-based mass loading limit (kg/yr) 

 Csol = chemical solubility (or density for chemicals miscible with water) (kg/ m3) 

 Rw,avg = average solid-water partition coefficient, which accounts for equilibrium partitioning 

between the solid, aqueous, and air phases (unitless) 

 Vtot,avg = average landfill volume (m3) 

 Tlf,avg = average landfill operating life (yr). 

The solid-water partition coefficient, landfill volume, and landfill operating life vary by simulation 

based on estimated national variability; thus, average values are used for these parameters. For 

chemicals fully miscible with water, the chemical’s density was used in place of the solubility in the 

calculation above (at a concentration equal to the chemical’s density, the liquid would be pure 

chemical). If the risk-based mass loading limit exceeded the solubility-based value, the result was 

flagged and the solubility-based mass loading limit was reported. 

5.2 Mass Loading Limits 

Table 5-1 shows the RB-MLLs based on noncancer effects at the 90th percentile of the 

distribution for each solvent and landfill liner combination calculated using the above steps. Table 

5-2 shows the RB-MLLs based on noncancer effects at the 50th percentile. These tables also specify 

the limiting pathway (pathway/receptor) resulting in the maximum 90th and 50th percentile risks and 

the associated mass loading limits. Tables 5-3 and 5-4 present the RB-MLLs for chemicals with 

potential cancer effects. These tables compare the RB-MLLs for cancer risk thresholds of 1E-4, 1E5, and 1E-6, as well as a noncancer risk threshold of HQ=1. 

Do not cite or quote. 5-4 
Review Draft Section 5.0 Landfill Mass Loading Rates 

Section 4.1 provides the 90th and 50th percentile risk results for all pathways, receptors, and 

endpoints, and for each solvent and liner combination (not just the limiting pathway). Appendix A

provides the results (risk and mass loading rates) for other percentiles (75th, 95th, and 99th). All of 

the risk values are associated with the constant unit mass loading rate of 1 kg/yr. 

Table 5-1. RB-MLLs for Noncarcinogens at the 90th Percentile 

No Liner Composite 

CAS Chemical MLL (kg/yr)a

Limiting 

Pathwayb MLL (kg/yr)a

Limiting 

Pathwayb

79-00-5 1,1,2-Trichloroethane 1.2E+02 DW-Ch 1.0E+05 DW-Ch 

110-80-5 2-Ethoxyethanol 2.3E+04 DW-Ch 8.1E+07 DW-Ch 

67-64-1 Acetone 2.8E+04 DW-Ch 1.1E+08 S DW-Ch 

71-43-2 Benzene 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A 

71-36-3 Butanol 1.2E+04 DW-Ch 1.2E+07 DW-Ch 

108-90-7 Chlorobenzene 5.8E+01 SH-A 2.6E+04 SH-A 

1319-77-3 Cresols (total) 1.4E+03 Comb(DW)-Ch 1.4E+06 Comb(DW)-Ch 

108-94-1 Cyclohexanone 9.9E+04 DW-Ch 7.5E+06 S DW-Ch 

95-50-1 Dichlorobenzene, 1,2- 7.1E+02 SH-A 1.4E+06 SH-A 

141-78-6 Ethyl acetate 5.3E+05 DW-Ch 4.4E+08 S DW-Ch 

100-41-4 Ethyl benzene 2.1E+03 DW-Ch 2.6E+06 DW-Ch 

78-83-1 Isobutanol 5.0E+04 DW-Ch 2.5E+07 S DW-Ch 

67-56-1 Methanol 4.9E+03 DW-Ch 1.2E+07 DW-Ch 

78-93-3 Methyl ethyl ketone 1.9E+04 Comb(DW)-Ch 3.0E+07 S AA-Ch 

108-10-1 Methyl isobutyl ketone 6.2E+03 DW-Ch 1.4E+07 S DW-Ch 

75-09-2 Methylene chloride 8.0E+02 SH-A 9.7E+05 AA-Ch 

127-18-4 Tetrachloroethylene 1.3E+02 DW-Ch 5.6E+04 DW-Ch 

108-88-3 Toluene 1.1E+03 DW-Ch 6.1E+05 DW-Ch 

79-01-6 Trichloroethylene 7.3E+02 SH-A 4.4E+05 SH-A 

1330-20-7 Xylene (mixed 

isomers) 

4.8E+02 SH-A 5.4E+05 SH-A 

a

 S = Based on solubility limit. 

b

 Limiting Pathway is denoted as Pathway-Receptor, where: 

AA = ambient air; Comb = combined drinking water, shower inhalation, shower/bath dermal; DM = 

shower/bath dermal; DW = drinking water; SH = shower inhalation. 

For combined (Comb) risks, the associated limiting pathway is provided in parentheses. 

A = Adult, Ch = Child. 

 

Do not cite or quote. 5-5 
Review Draft Section 5.0 Landfill Mass Loading Rates 

Table 5-2. RB-MLLs for Noncarcinogens at the 50th Percentile 

No Liner Composite 

CAS Chemical MLL (kg/yr)a

Limiting 

Pathwayb MLL (kg/yr)a

Limiting 

Pathwayb

79-00-5 1,1,2-Trichloroethane 4.8E+03 DW-Ch 5.9E+07 S NA 

110-80-5 2-Ethoxyethanol 2.1E+06 DW-Ch 1.4E+09 S AA-Ch 

67-64-1 Acetone 8.4E+05 DW-Ch 1.1E+08 S AA-Ch 

71-43-2 Benzene 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch 

71-36-3 Butanol 5.9E+05 DW-Ch 4.9E+08 S DW-Ch 

108-90-7 Chlorobenzene 3.1E+03 SH-A 1.4E+06 AA-Ch 

1319-77-3 Cresols (total) 9.9E+04 Comb(DW)-Ch 7.6E+07 S AA-Ch 

108-94-1 Cyclohexanone 3.7E+06 DW-Ch 7.5E+06 S DW-Ch 

95-50-1 Dichlorobenzene, 1,2- 4.2E+04 SH-A 4.0E+06 S AA-Ch 

141-78-6 Ethyl acetate 4.4E+08 S DW-Ch 4.4E+08 S NA 

100-41-4 Ethyl benzene 1.2E+05 DW-Ch 8.0E+06 S AA-Ch 

78-83-1 Isobutanol 2.4E+06 DW-Ch 2.5E+07 S DW-Ch 

67-56-1 Methanol 2.7E+05 DW-Ch 6.9E+08 S AA-Ch 

78-93-3 Methyl ethyl ketone 8.0E+05 Comb(DW)-Ch 3.0E+07 S AA-Ch 

108-10-1 Methyl isobutyl ketone 3.0E+05 DW-Ch 1.4E+07 S AA-Ch 

75-09-2 Methylene chloride 3.4E+04 SH-A 6.4E+06 AA-Ch 

127-18-4 Tetrachloroethylene 5.6E+03 DW-Ch 4.0E+06 S AA-Ch 

108-88-3 Toluene 4.9E+04 DW-Ch 1.3E+07 S AA-Ch 

79-01-6 Trichloroethylene 3.9E+04 SH-A 9.3E+06 AA-Ch 

1330-20-7 Xylene (mixed 

isomers) 

2.3E+04 SH-A 8.5E+06 AA-Ch 

a

 S = Based on solubility limit. 

b

 Limiting Pathway is denoted as Pathway-Receptor, where: 

AA = ambient air; Comb = combined drinking water, shower inhalation, shower/bath dermal; DM = 

shower/bath dermal; DW = drinking water; SH = shower inhalation. 

For combined (Comb) risks, the associated limiting pathway is provided in parentheses. 

A = Adult, Ch = Child. 

Do not cite or quote. 5-6 
Review Draft Section 5.0 Landfill Mass Loading Rates 

Table 5-3. RB-MLLs for Carcinogens at the 90th Percentile 

No Liner Composite 

Risk Threshold MLL (kg/yr)a Limiting Pathwayb MLL (kg/yr)a Limiting Pathwayb

79-00-5: 1,1,2-Trichloroethane 

10-4 1.1E+02 Comb(SH)-A 1.1E+05 Comb(DW)-A 

10-5 1.1E+01 Comb(SH)-A 1.1E+04 Comb(DW)-A 

10-6 1.1E+00 Comb(SH)-A 1.1E+03 Comb(DW)-A 

HQ=1 1.2E+02 DW-Ch 1.0E+05 DW-Ch 

71-43-2: Benzene (high)c

10-4 5.0E+01 Comb(SH)-A 3.6E+04 Comb(SH)-A 

10-5 5.0E+00 Comb(SH)-A 3.6E+03 Comb(SH)-A 

10-6 5.0E-01 Comb(SH)-A 3.6E+02 Comb(SH)-A 

HQ=1 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A 

71-43-2: Benzene (low)c

10-4 1.8E+02 Comb(SH)-A 1.3E+05 Comb(SH)-A 

10-5 1.8E+01 Comb(SH)-A 1.3E+04 Comb(SH)-A 

10-6 1.8E+00 Comb(SH)-A 1.3E+03 Comb(SH)-A 

HQ=1 2.2E+01 Comb(DW)-A 1.4E+04 Comb(DW)-A 

75-09-2: Methylene Chloride 

10-4 1.1E+03 Comb(DW)-A 2.4E+06 AA-A 

10-5 1.1E+02 Comb(DW)-A 2.4E+05 AA-A 

10-6 1.1E+01 Comb(DW)-A 2.4E+04 AA-A 

HQ=1 8.0E+02 SH-A 9.7E+05 AA-Ch 

127-18-4: Tetrachloroethylene (high) c

10-4 1.7E+01 Comb(DW)-A 8.2E+03 Comb(DW)-A 

10-5 1.7E+00 Comb(DW)-A 8.2E+02 Comb(DW)-A 

10-6 1.7E-01 Comb(DW)-A 8.2E+01 Comb(DW)-A 

HQ=1 1.3E+02 DW-Ch 5.6E+04 DW-Ch 

127-18-4: Tetrachloroethylene (low)c

10-4 2.0E+01 Comb(DW)-A 9.8E+03 Comb(DW)-A 

10-5 2.0E+00 Comb(DW)-A 9.8E+02 Comb(DW)-A 

10-6 2.0E-01 Comb(DW)-A 9.8E+01 Comb(DW)-A 

HQ=1 1.3E+02 DW-Ch 5.6E+04 DW-Ch 

79-01-6: Trichloroethylene 

10-4 2.2E+02 Comb(SH)-A 1.3E+05 Comb(DW)-A 

10-5 2.2E+01 Comb(SH)-A 1.3E+04 Comb(DW)-A 

10-6 2.2E+00 Comb(SH)-A 1.3E+03 Comb(DW)-A 

HQ=1 7.3E+02 SH-A 4.4E+05 SH-A 

Do not cite or quote. 5-7 
Review Draft Section 5.0 Landfill Mass Loading Rates 

a

 S = Based on solubility limit. 

b

 Limiting Pathway is denoted as Pathway-Receptor, where: 

AA = ambient air; Comb = combined drinking water, shower inhalation, shower/bath dermal; DM = 

shower/bath dermal; DW = drinking water; SH = shower inhalation. 

For combined (Comb) risks, the associated limiting pathway is provided in parentheses. 

A = Adult, Ch = Child. c

 Two estimates are provided for benzene and tetrachloroethylene cancer risks to reflect the high and low ends of 

the range of benchmarks. 

Do not cite or quote. 5-8 
Review Draft Section 5.0 Landfill Mass Loading Rates 

Table 5-4. RB-MLLs for Carcinogens at the 50th Percentile 

No Liner Composite 

Risk Threshold MLL (kg/yr)a Limiting Pathwayb MLL (kg/yr)a Limiting Pathwayb

79-00-5: 1,1,2-Trichloroethane 

10-4 5.9E+03 Comb(SH)-A 3.8E+06 AA-A 

10-5 5.9E+02 Comb(SH)-A 3.8E+05 AA-A 

10-6 5.9E+01 Comb(SH)-A 3.8E+04 AA-A 

HQ=1 4.8E+03 DW-Ch 5.9E+07 S NA 

71-43-2: Benzene (high)c

10-4 2.9E+03 Comb(DW)-A 1.4E+06 AA-A 

10-5 2.9E+02 Comb(DW)-A 1.4E+05 AA-A 

10-6 2.9E+01 Comb(DW)-A 1.4E+04 AA-A 

HQ=1 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch 

71-43-2: Benzene (low)c

10-4 1.0E+04 Comb(DW)-A 4.8E+06 AA-A 

10-5 1.0E+03 Comb(DW)-A 4.8E+05 AA-A 

10-6 1.0E+02 Comb(DW)-A 4.8E+04 AA-A 

HQ=1 1.0E+03 Comb(SH)-A 4.9E+05 AA-Ch 

75-09-2: Methylene Chloride 

10-4 5.4E+04 Comb(SH)-A 1.1E+07 S AA-A 

10-5 5.4E+03 Comb(SH)-A 2.2E+06 AA-A 

10-6 5.4E+02 Comb(SH)-A 2.2E+05 AA-A 

HQ=1 3.4E+04 SH-A 6.4E+06 AA-Ch 

127-18-4: Tetrachloroethylene (high) c

10-4 9.5E+02 Comb(DW)-A 1.7E+06 AA-A 

10-5 9.5E+01 Comb(DW)-A 1.7E+05 AA-A 

10-6 9.5E+00 Comb(DW)-A 1.7E+04 AA-A 

HQ=1 5.6E+03 DW-Ch 4.0E+06 S AA-Ch 

127-18-4: Tetrachloroethylene (low) c

10-4 1.1E+03 Comb(DW)-A 1.5E+07 AA-A 

10-5 1.1E+02 Comb(DW)-A 1.5E+06 AA-A 

10-6 1.1E+01 Comb(DW)-A 1.5E+05 AA-A 

HQ=1 5.6E+03 DW-Ch 4.0E+06 S AA-Ch 

79-01-6: Trichloroethylene 

10-4 1.3E+04 Comb(SH)-A 5.1E+06 AA-A 

10-5 1.3E+03 Comb(SH)-A 5.1E+05 AA-A 

10-6 1.3E+02 Comb(SH)-A 5.1E+04 AA-A 

HQ=1 3.9E+04 SH-A 9.3E+06 AA-Ch 

Do not cite or quote. 5-9 
Review Draft Section 5.0 Landfill Mass Loading Rates 

a

 S = Based on solubility limit. 

b

 Limiting Pathway is denoted as Pathway-Receptor, where: 

AA = ambient air; Comb = combined drinking water, shower inhalation, shower/bath dermal; DM = 

shower/bath dermal; DW = drinking water; SH = shower inhalation. 

For combined (Comb) risks, the associated limiting pathway is provided in parentheses. 

A = Adult, Ch = Child. c

 Two estimates are provided for benzene and tetrachloroethylene cancer risks to reflect the high and low ends of 

the range of benchmarks. 

Do not cite or quote. 5-10 
Review Draft Section 6.0 References 

6.0 References 

Aivaliotis, V., I. Dokas, M. Hatzigiannakou, and D. Panagiotakopoulos. 2004. Functional 

relationships of landfill and landraise capacity with design and operation parameters. Waste 

Management and Research 22(4):283–290. 

Aronson, D., and P.H. Howard. 1997. Anaerobic Biodegradation of Organic Chemicals in 

Groundwater: A Summary of Field and Laboratory Studies. Prepared for the American 

Petroleum Institute, the Chemical Manufacturer’s Association the National Council of the 

Paper Industry for Air and Stream Improvement, the Edison Electric Institute, and the 

American Forest and Paper Association. North Syracuse, NY: Syracuse Research 

Corporation. Available at 

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/pdf/risk/reports/s0535.pdf. 

ATSDR (Agency for Toxic Substances and Disease Registry). 2007. Minimal Risk Levels (MRLs) 

for Hazardous Substances. Available at http://www.atsdr.cdc.gov/mrls.html 

Aubertin, M., Aachib, M., and Authier, K. 2000. Evaluation of diffusive gas flux through covers 

with a GCL. Geotextiles and Geomembranes 18:1–19. 

Barlaz, M. 2006. “Topics relevant to development and parameterization of the LFCR landfill source 

model.” Phone and e-mail communications from Dr. Morton Barlaz, Professor, Water 

Resources/Environmental Engineering, North Carolina State University, to Michael I. 

Lowry, RTI. July through December. 

Bartelt-Hunt, S. 2006. “Topics relevant to development and parameterization of the LFCR landfill 

source model.” Phone and e-mail communications from Dr. Shannon L. Bartelt-Hunt, 

Assistant Professor, Department of Civil Engineering, University of Nebraska-Lincoln, to 

Michael I. Lowry, RTI. July through December. 

Bartelt-Hunt, Shannon L., Susan E. Burns and James A. Smith. 2003. Nonionic organic solute 

sorption onto two organobentonites as a function of organic-carbon content. Journal of 

Colloid and Interface Science 266(2):251-258. 

Bartelt-Hunt, S.L., D.R.U. Knappe, P. Kjeldsen, and M.A. Barlaz. 2006. Fate of chemical warfare 

agents and toxic industrial chemicals in landfills. Environmental Science and Technology 

40(13):4219–4225. 

Benson, C.H., D.E. Daniel, and G.P. Boutwell. 1999. Field performance of compacted clay liners. 

Journal of Geotechnical and Geoenvironmental Engineering 125(5):390–403. 

Cabbar, H. C., and A. Bostancı. 2001. Moisture Effect on Stationary Diffusion of Organic Vapors in 

Unsaturated Sand, Journal of Hazardous Materials 82: 313. 

CalEPA (California Environmental Protection Agency). 2007. CalEPA - OEHHA Toxicity Criteria 

Database. Office of Environmental Health Hazard Assessment, Berkeley, CA. Values are 

available in database at http://www.oehha.org/risk/ChemicalDB/index.asp

Coburn, J. 1996. “Emission Flux Equations for Showering.” Memorandum from Jeff Coburn, RTI, 

to Rebecca Daiss, U.S. Environmental Protection Agency, Office of Solid Waste, 

Washington, DC. July 1. 

Do not cite or quote. 6-1 
Review Draft Section 6.0 References 

Cullen, A.C., and H.C. Frey. 1999. Probabilistic Techniques in Exposure Assessment: A Handbook 

for Dealing with Variability and Uncertainty in Models and Inputs. New York: Plenum 

Press. 

Edil T.B. 2003. A review of aqueous-phase VOC transport in modern landfill liners. Waste 

Management 23(7):561-571. 

Efron, B., and R.J. Tibshirani. 1993. An Introduction to the Bootstrap. Chapman and Hall, New 

York. 

EREF (Environmental Research and Education Foundation). 1999. Life-Cycle Inventory of Modern 

Municipal Solid Waste Landfills. Environmental Research and Education Foundation, 

Washington, DC. June. 

ERG. 2003. Analysis of Cleaning Materials Reported in the EPA Miscellaneous Metal Parts and 

Products Survey Database. Memorandum from Brian Palmer (ERG) to Kim Teal (U.S. 

Environmental Protection Agency). August 2003. Document No. OAR-2003-0116-0004. 

Foose, Gary J. 2002. Transit-time design for diffusion through composite liners. Journal of 

geotechnical and geoenvironmental engineering 128(7): 590-601. 

Fuller, E. N., P. D. Schettler, and J. C. Giddings. 1966. A New Method for Prediction of Binary GasPhase Diffusion Coefficients. Industrial Engineering and Chemistry 58:19–27.

Fungaroli, A. and R. Steiner. 1979. Investigation of Sanitary Landfill Behavior. Volume 1, Final 

Report. EPA 600/2-79-053a. 

Hoffman, F.O., and J.S. Hammonds. 1994. Propagation of uncertainty in risk assessments: The need 

to distinguish between uncertainty due to lack of knowledge and uncertainty due to 

variability. Risk Analysis 14(5):707–712. 

Howard, P.H., R.S. Boethling, W.F., Jarvis, W.M. Meylan, and E.M. Michalenko. 1991. Handbook 

of Environmental Degradation Rates. Boca Raton, FL: Lewis Publishers. 

Itakura T, Airey DW. and C.J. Leo. 2003. The Diffusion and Sorption of Volatile Organic 

Compounds Through Kaolinitic Clayey Soils. Journal of Contaminant Hydrology 65: 219-

243. 

Jury, W.A., W.F. Spencer, and W.J. Farmer. 1983. Behavior assessment model for trace organics in 

soil: 1. model description. Journal of Environmental Quality 12(4):558–564. 

Jury, W.A., D. Russo, G. Streile, and H. El Abd. 1990. Evaluation of volatilization by organic 

chemicals residing below the soil surface. Water Resources Research 26(1):13–20. 

Khandelwal, A., and Rabideau, A. J. 2000. Amendment of soil/bentonite slurry walls with natural 

humus, Journal of Contaminant Hydrology 25:267-282. 

Kim, J. Y., M.-C. Shin, J.R. Park, and K. Nam. 2003. Effect of soil solids concentration in batch 

tests on the partition coefficients of organic pollutants in landfill liner-soil materials. Journal 

of Material Cycles and Waste Management 5(1):55–62. 

Kjeldsen, P., and T.H. Christensen. 2001. A simple model for the distribution and fate of organic 

chemicals in a landfill: MOCLA. Waste Management and Research 19(3):201–216. 

Do not cite or quote. 6-2 
Review Draft Section 6.0 References 

Kjeldsen, P., M.A. Barlaz, A.P. Rocker, A. Baun, A. Ledin, and T.H. Christensen. 2002. Present and 

long-term composition of MSW landfill leachate: A review. Critical Reviews in 

Environmental Science and Technology 32(4):297–336. 

Lake, C.B. and Rowe, R.K. 2005. A comparative assessment of volatile organic compound (VOC) 

sorption to various types of potential GCL bentonites. Geotextiles and Geomembranes 

23(4):323-347. 

Little, J.C. 1992a. Applying the two resistance theory to contaminant volatilization in showers. 

Environmental Science and Technology 26(7):1341–1349. 

Little, J.C. 1992b. Correspondence. Comment on “Human exposure to volatile organic compounds 

in household tap water: The indoor inhalation pathway.” Environmental Science and 

Technology 26(4):836–837. 

Lowry, M.I., S.L.Bartelt-Hunt, S. M. Beaulieu, and M.A. Barlaz. 2008. Development of a Coupled 

Reactor Model for Prediction of Organic Contaminant Fate in Landfills. Environmental 

Science and Technology 42(19):7444–7451. 

Lyman, W. J., W. J. Reehl, and D. H. Rosenblatt. 1990. Handbook of Chemical Property Estimation 

Methods: Environmental Behavior of Organic Compounds. Washington, DC: American 

Chemical Society. 

McKone, T.E. 1987. Human exposure to volatile organic compounds in household tap water: The 

indoor inhalation pathway. Environmental Science and Technology 21(12):1194–1201. 

Mokhtari, A., and H.C. Frey. 2005. Sensitivity analysis of a two-dimensional probabilistic risk 

assessment model using analysis of variance. Risk Analysis 25(6):1511–1529. 

Nauta, M.J. (2000). Separation of uncertainty and variability in quantitative microbial risk 

assessment models. International Journal of Food Microbiology 57(1):9–18. 

Newell, C.J., L.P. Hopkins, and P.B. Bedient. 1990. A hydrogeologic database for groundwater 

modeling. Ground Water. September. 

NLM (National Library of Medicine). 2006. TOXNET: Toxicology Data Network. Available at 

http://toxnet.nlm.nih.gov. Accessed December 2006. 

Park, J.K., Sakti, J.P., Hoopes, J.A. 1996. Transport of Aqueous Organic Compounds in 

Thermoplastic Geomembranes. II. Mass Flux Estimates and Practical Implications,” Jour. of 

Environ. Eng., ASCE 122(9):807-813. 

Perry, R. H., and D. W. Green. 1997. Perry’s Chemical Engineers’ Handbook. 7th Edition. New 

York: McGraw-Hill. 

Qian, X., R.M. Koerner, and D.H. Gray. 2002. Geotechnical Aspects of Landfill Design and 

Construction. Upper Saddle River, NJ: Prentice Hall. 

RTI (Research Triangle Institute). 2003. Risk Assessment Technical Background Document for the 

Dye and Pigment Industry Hazardous Waste Listing Determination. Prepared for U.S. 

Environmental Protection Agency, Office of Solid Waste, Washington, DC. 

RTI (Research Triangle Institute). 2007. Human and Ecological Risk Assessment of Coal 

Combustion Wastes. Draft. Prepared for U.S. Environmental Protection Agency, Office of 

Solid Waste, Washington, DC. August 6. 

Do not cite or quote. 6-3 
Review Draft Section 6.0 References 

SAIC. 2008. Landfill Loading Calculations for Solvent-Contaminated Wipes. Prepared for the U.S. 

Environmental Protection Agency, Office of Solid Waste, Washington, DC. February, 2008. 

Sangam, H. P., and R. K. Rowe. 2001. Migration of dilute aqueous organic pollutants through HDPE 

geomembranes. Geotextiles and Geomembranes 19(6):329–357. 

Schroeder, P.R., T.S. Dozier, P.A. Zappi, B.M. McEnroe, J.W. Sjostrom, and R.L. Peyton. 1994. The 

Hydrologic Evaluation of Landfill Performance (HELP) Model: Engineering Documentation 

for Version 3. EPA/600/R-94/168b. U.S. Environmental Protection Agency, Office of 

Research and Development, Washington, DC. 

Sivak, M. 2006. Guidance for the Calculation of Cancer Non-Cancer Risk from Inhalation 

Exposures: An Update. Presented at the EPA Risk Assessors Annual Conference. May 3. 

Steinberg, S.M., J.S. Schmeltzer, D.K. Kreamer. 1996. Sorption of Benzene and Trichloroethylene 

(TCE) on a Desert Soil: Effects of Moisture and Organic Matter. Chemosphere 33(5):961–

980. 

Suter, G.W. II, and C.L. Tsao. 1996. Toxicological Benchmarks for Screening Potential 

Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision. ES/ER/TM-96/R2. 

Prepared for the U.S. Department of Energy, Washington, DC. Available at 

http://www.esd.ornl.gov/programs/ecorisk/documents/tm96r2.pdf. 

TetraTech, Inc. 2001. Characterization of infiltration rate data to support groundwater modeling 

efforts (draft). Prepared for the U.S. Environmental Protection Agency, Office of Solid 

Waste, Washington, DC, Contract No. 68-W6-0061. May. 

Themelis, N.J., and P. Ulloa. 2005. Capture and Utilization of Landfill Gas, Renewable Energy 

2005, World Renewable Energy Network, p. 77–81. 

U.S. EPA (Environmental Protection Agency), 1987. Industrial Subtitle D Facility Study (Telephone 

Survey). U.S. EPA, Office of Solid Waste, Washington, DC, October. 

U.S. EPA (Environmental Protection Agency). 1993a. Parameter Values for Developing Nationwide 

Regulations with the EPA’s Composite Model for Landfills (EPACML): Toxicity 

Characteristics Rule. U.S. EPA, Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1993b. Environmental Fate Constants for Organic 

Chemicals Under Consideration for EPA’s Hazardous Waste Identification Projects. 

EPA/600/SR-93/132. U.S. EPA, Athens, GA. 

U.S. EPA (Environmental Protection Agency). 1994a. Methods for Derivation of Inhalation 

Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90-066F. 

U.S. EPA, Environmental Criteria and Assessment Office, Office of Health and 

Environmental Assessment, Office of Research and Development, Research Triangle Park, 

NC. 

U.S. EPA (Environmental Protection Agency).1994b. EPA's Alternative Control Techniques 

Document—Industrial Cleaning Solvents. EPA-453/R-94-015. February. 

U.S. EPA (Environmental Protection Agency). 1995a. User’s Guide for the Industrial Source 

Complex (ISC3) Dispersion Models. EPA454/B-95-003b. U.S. EPA, Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. 

Do not cite or quote. 6-4 
Review Draft Section 6.0 References 

U.S. EPA (Environmental Protection Agency). 1995b. Guidance for Risk Characterization. U.S. 

EPA, Science Policy Council. February. Available at http://www.epa.gov/spc/ 

pdfs/rcguide.pdf. 

U.S. EPA (Environmental Protection Agency). 1986. Guidelines for carcinogen risk assessment. 

Federal Register 51(185):33992–34003. 

U.S. EPA (Environmental Protection Agency). 1997a. Emission Factor Documentation For AP-42 

Section 2.4, Municipal Solid Waste Landfills, Revised. U.S. EPA, Office of Air Quality 

Planning and Standards, Office of Air and Radiation, Research Triangle Park, NC. Available 

at http://www.epa.gov/ttn/chief/ap42/ch02/bgdocs/b02s04.pdf. 

U.S. EPA (Environmental Protection Agency). 1997b. EPACMTP: User’s Guide. U.S. EPA, Office 

of Solid Waste, Washington, DC. Available at 

http://www.epa.gov/waste/nonhaz/industrial/tools/cmtp/userguid.pdf. 

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume I, 

General Factors. EPA/600/P-95/002Fa. U.S. EPA, Office of Research and Development, 

Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1997d. Exposure Factors Handbook, Volume II, 

Food Ingestion Factors. EPA/600/P-95/002Fb. U.S. EPA, Office of Research and 

Development, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1997e. Exposure Factors Handbook, Volume III, 

Activity Factors. EPA/600/P-95/002Fc. U.S. EPA, Office of Research and Development, 

Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1997f. Health Effects Assessment Summary Tables 

(HEAST). FY 1997 Update. EPA-540-R-97-036. U.S. EPA, National Center for 

Environmental Protection, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 1997g. Guiding Principles for Monte Carlo Analysis. 

EPA/630/R-97/001. March. 

U.S. EPA (Environmental Protection Agency). 1998. Cleaner technologies substitutes assessment: 

professional fabricare processes. U.S. EPA, Office of Pollution Prevention and Toxics, 

Washington, DC, EPA 744-B-98-001. 

U.S. EPA (Environmental Protection Agency). 1999a. Source modules for non-wastewater waste 

management units (land application units, waste piles, and landfills), background and 

implementation for the multimedia, multi-pathway, and multi-receptor risk assessment 

(3MRA) for HWIR 99. U.S. EPA, Washington, DC. Available at 

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/pdf/risk/modules/s0056.pdf. 

U.S. EPA (Environmental Protection Agency). 1999b. Anaerobic Biodegradation Rates of Organic 

Chemicals in Groundwater: A Summary of Field and Laboratory Studies. U.S. EPA, Office 

of Solid Waste, Washington, DC. Available at 

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/pdf/risk/reports/s0535.pdf. 

U.S. EPA (Environmental Protection Agency). 2000. RCRA Delisting Technical Support Document 

(DTSD). EPA906-D-98-001. Interim Final. U.S. EPA, Office of Solid Waste. Prepared by 

Multimedia Planning and Permitting Division, U. S. EPA, Region 6, Dallas, Texas. Available 

at http://www.epa.gov/region6/6pd/rcra_c/pd-o/dtsd.htm. 

Do not cite or quote. 6-5 
Review Draft Section 6.0 References 

U.S. EPA (Environmental Protection Agency). 2001a. Risk Assessment Guidance for Superfund 

Volume 3 Part A: Process for Conducting Probabilistic Risk Assessment (RAGS 3A). EPA 

540-R-02-002. Available at www.epa.gov/superfund/RAGS3A/index.htm. December. 

U.S. EPA (Environmental Protection Agency). 2001b. Risk Assessment Technical Background 

Document for the Paint and Coatings Hazardous Waste Listing Determination. Office of 

Solid Waste, Washington, DC. January. 

U.S. EPA (Environmental Protection Agency). 2002a. Industrial Waste Air Model Technical 

Background Document. U.S. EPA, Office of Solid Waste, Washington, DC. Available at 

http://www.epa.gov/waste/nonhaz/industrial/tools/iwair/index.htm#techdoc. 

U.S. EPA (Environmental Protection Agency). 2002b. Superfund Chemical Data Matrix (SCDM). 

Superfund Internet Site, Washington, DC. Available at 

http://www.epa.gov/superfund/sites/npl/hrsres/tools/scdm.htm. 

U.S. EPA (Environmental Protection Agency). 2003a. EPA’s Composite Model for Leachate 

Migration with Transformation Products (EPACMTP) Parameters/Data Background 

Document. EPA530-R-03-003. U.S. EPA, Office of Solid Waste, Washington, DC. 

Available at http://www.epa.gov/waste/nonhaz/industrial/tools/cmtp/index.htm#data. 

U.S. EPA (Environmental Protection Agency). 2003b. Risk Assessment Technical Background 

Document for the Dye and Pigment Industry Hazardous Waste Listing Determination. U.S. 

EPA, Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2003c. EPACMTP Technical Background Document. 

U.S. EPA, Office of Solid Waste, Washington, DC. Available at 

http://www.epa.gov/waste/nonhaz/industrial/tools/cmtp/v2_tbd.pdf. 

U.S. EPA (Environmental Protection Agency). 2003d. Addendum to the EPACMTP Technical 

Background Document. U.S. EPA, Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2003e. Multimedia, Multipathway, and Multireceptor 

Risk Assessment (3MRA) Modeling System. Volume I: Modeling System and Science. U.S. 

EPA, Office of Solid Waste, Washington, DC, and Office of Research and Development, 

National Exposure Research Laboratory, Athens, GA. Available at 

http://www.epa.gov/ceampubl/mmedia/3mra/. 

U.S. EPA (Environmental Protection Agency). 2003f. OSWER Directive 9285.7-53. Letter from 

Michael B. Cook, Director, U.S. Environmental Protection Agency, Office of Superfund 

Remediation and Technology Innovation, Office of Solid Waste and Emergency Response, 

Washington, DC, to Superfund National Policy Managers, Regions 1–10. December 5. 

U.S. EPA (Environmental Protection Agency). 2004a. EPA's Multimedia Multipathway and 

Multireceptor Risk Assessment (3MRA) Modeling System: A Review by the 3MRA Review 

Panel of the EPA Science Advisory Board. EPA-SAB-05-003. October. Available at 

http://yosemite.epa.gov/sab/sabproduct.nsf/99390EFBFC255AE885256FFE00579745/$File/

SAB-05-003_unsigned.pdf. 

U.S. EPA (Environmental Protection Agency). 2004b. Risk Assessment Guidance for Superfund 

Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal 

Risk Assessment). Final. U.S. EPA, Office of Superfund Remediation and Technology 

Innovation, Washington, DC. Available at http://www.epa.gov/oswer/ riskassessment/ragse/. 

Do not cite or quote. 6-6 
Review Draft Section 6.0 References 

U.S. EPA (Environmental Protection Agency). 2005a. Landfill Gas Emissions Model (LandGEM) 

Version 3.02 User’s Guide. EPA-600/R-05/047. U.S. EPA, Office of Research and 

Development, Morrisville, NC. Available at http://www.epa.gov/ttncatc1/products.html. 

U.S. EPA (Environmental Protection Agency). 2005b. Guidelines for Carcinogen Risk Assessment. 

EPA/630/P-03/001B. U.S. EPA, Risk Assessment Forum. March. Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2005c. Human Health Risk Assessment Protocol for 

Hazardous Waste Combustion Facilities. EPA/530/R-05/006. U.S. EPA, Office of Solid 

Waste and Emergency Response. September. Washington, DC. Available at 

http://www.epa.gov/waste/hazard/tsd/td/combust/risk.htm. 

U.S. EPA (Environmental Protection Agency). 2006a. Landfill Methane Outreach Program 

Database (LMOP). http://www.epa.gov/lmop/index.htm. U.S. EPA, Office of Air and 

Radiation, Washington, DC. 

U.S. EPA (Environmental Protection Agency), 2006b. 2005 Urban Air Toxics Monitoring Program 

(UATMP). EPA-454/R-07-001. U.S. EPA, Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. Available at 

http://www.epa.gov/ttnamti1/files/ambient/airtox/2005_uatmp_main_full.pdf. 

U.S. EPA (Environmental Protection Agency). 2006c.Child-Specific Exposure Factors Handbook 

(External Review Draft). EPA-600/R-06-096a. U.S. EPA, National Center for Environmental 

Assessment, Cincinnati, OH. September. Available at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56747. 

U.S. EPA (Environmental Protection Agency). 2006d. National Recommended Water Quality 

Criteria. Office of Water, Office of Science and Technology. 2006. 

U.S. EPA (Environmental Protection Agency). 2006e. National Analysis: The National Biennial 

RCRA Hazardous Waste Report (based on 2003 data). Available at 

http://www.epa.gov/osw/inforesources/data/br03/national03.pdf. 

U.S. EPA (Environmental Protection Agency). 2007. Integrated Risk Information System (IRIS). 

U.S. EPA, National Center for Environmental Assessment, Office of Research and 

Development, Washington, DC. Available at http://www.epa.gov/iris/. 

U.S. EPA (Environmental Protection Agency). 2008. IRIS Toxicological Review of 

Tetrachloroethylene (Perchloroethylene) (External Review Draft). U.S. Environmental 

Protection Agency, Washington, DC, EPA/635/R-08/011A. 

Do not cite or quote. 6-7